Attrition Case Study for Frito Lay
A case study into the work force data set for MSDS 6306 by Renu Karthikeyan
CaseStudy2 Attrition
Renu Karthikeyan
November/December 2023
Statement of Purpose:
I am pleased to present this case study on Frito Lay attrition, which aims to analyze and derive actionable insights from the workforce data at Frito Lay. The purpose of this study is to understand the factors influencing employee attrition, develop predictive models for attrition risk, and evaluate the performance of the predictive models.
Objectives:
- Data Exploration and Visualization: Conduct a thorough exploration of the available workforce data to gain insights into the distribution of various attributes.Visualize key trends and patterns related to attrition, employee demographics, and other relevant factors.
- Predictive Modeling: Use K-nearest neighbors (KNN) and Naive Bayes, to build predictive models for employee attrition.Evaluate the performance of each model and identify the most effective approach in predicting attrition risk. Create linear regression model to predict salary for employees, given all other predictors.
- Shiny App Development: Create an interactive Shiny app to visualize and communicate the insights derived from the analysis.Provide a user-friendly platform for stakeholders to explore the data and understand the implications of the findings.
- Communication and Collaboration: Effectively communicate the results and recommendations to stakeholders through clear and concise reports and presentations.
Load and Install Libraries
library(dplyr)
library(ggplot2)
library(caret)
library(aws.s3)
library(RCurl)
library(readr)
library(base)
library(tidyverse)
library(naniar)
library(class)
library(GGally)
library(e1071)
library(car)
library(fastDummies)Set AWS credentials, and Load in Datasets from Amazon S3 Bucket
Here, loading in the data from the AWS S3 Bucket. I did some slight clean up of the data, to exclude the “Over18” column in the original data set and the Attrition test data set. The column name for ID in the No Salary data set was not the same, so I adjusted that. Also, I did a check for any missing values. There are no missing values in any of the data sets, so there is no need for imputing or deleting of rows.
# Load the Attrition data set from S3
#s3_path <- "s3://msds.ds.6306.2/CaseStudy2-data.csv"
# Read the Attrition Data CSV file from S3
Attritiondata <- read.table(textConnection(getURL("https://msdsds6306.s3.us-east-2.amazonaws.com/CaseStudy2-data.csv")), sep =",", header =T)
head(Attritiondata,5)## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 13 4 Life Sciences 1 859
## 2 14 3 Medical 1 1128
## 3 18 2 Life Sciences 1 1412
## 4 1 4 Marketing 1 2016
## 5 2 1 Technical Degree 1 1646
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 73 3 2
## 2 3 Male 44 2 5
## 3 3 Male 60 3 3
## 4 3 Female 48 3 3
## 5 1 Female 32 3 1
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1 Sales Executive 4 Divorced 4403
## 2 Research Director 3 Single 19626
## 3 Manufacturing Director 4 Single 9362
## 4 Sales Executive 4 Married 10422
## 5 Research Scientist 4 Single 3760
## MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike
## 1 9250 2 Y No 11
## 2 17544 1 Y No 14
## 3 19944 2 Y No 11
## 4 24032 1 Y No 19
## 5 17218 1 Y Yes 13
## PerformanceRating RelationshipSatisfaction StandardHours StockOptionLevel
## 1 3 3 80 1
## 2 3 1 80 0
## 3 3 3 80 0
## 4 3 3 80 2
## 5 3 3 80 0
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## 1 8 3 2 5
## 2 21 2 4 20
## 3 10 2 3 2
## 4 14 3 3 14
## 5 6 2 3 6
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## 1 2 0 3
## 2 7 4 9
## 3 2 2 2
## 4 10 5 7
## 5 3 1 3#summary(Attritiondata)
vis_miss(Attritiondata) #checking for no missing values
#Read CSV "testing" files from S3
# Reading in of NoSalary Dataset from S3 Bucket
NoSalary<-read.table( textConnection(getURL
("https://msdsds6306.s3.us-east-2.amazonaws.com/CaseStudy2CompSet+No+Salary.csv"
)), sep=",", header=TRUE)
head(NoSalary,5)## ï..ID Age Attrition BusinessTravel DailyRate Department
## 1 871 43 No Travel_Frequently 1422 Sales
## 2 872 33 No Travel_Rarely 461 Research & Development
## 3 873 55 Yes Travel_Rarely 267 Sales
## 4 874 36 No Non-Travel 1351 Research & Development
## 5 875 27 No Travel_Rarely 1302 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 2 4 Life Sciences 1 1849
## 2 13 1 Life Sciences 1 995
## 3 13 4 Marketing 1 1372
## 4 9 4 Life Sciences 1 1949
## 5 19 3 Other 1 1619
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 1 Male 92 3 2
## 2 2 Female 53 3 1
## 3 1 Male 85 4 4
## 4 1 Male 66 4 1
## 5 4 Male 67 2 1
## JobRole JobSatisfaction MaritalStatus MonthlyRate
## 1 Sales Executive 4 Married 19246
## 2 Research Scientist 4 Single 17241
## 3 Sales Executive 3 Single 9277
## 4 Laboratory Technician 2 Married 9238
## 5 Laboratory Technician 1 Divorced 16290
## NumCompaniesWorked Over18 OverTime PercentSalaryHike PerformanceRating
## 1 1 Y No 20 4
## 2 3 Y No 18 3
## 3 6 Y Yes 17 3
## 4 1 Y No 22 4
## 5 1 Y No 11 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 1 3 80 1 7
## 2 1 80 0 5
## 3 3 80 0 24
## 4 2 80 0 5
## 5 1 80 2 7
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 5 3 7 7
## 2 4 3 3 2
## 3 2 2 19 7
## 4 3 3 5 4
## 5 3 3 7 7
## YearsSinceLastPromotion YearsWithCurrManager
## 1 7 7
## 2 0 2
## 3 3 8
## 4 0 2
## 5 0 7#summary(NoSalary)
vis_miss(NoSalary)
AttritionTest<- read.table(textConnection(getURL
("https://msdsds6306.s3.us-east-2.amazonaws.com/CaseStudy2CompSet+No+Attrition.csv"
)), sep=",", header=TRUE)
head(AttritionTest,5)## ID Age BusinessTravel DailyRate Department DistanceFromHome
## 1 1171 35 Travel_Rarely 750 Research & Development 28
## 2 1172 33 Travel_Rarely 147 Human Resources 2
## 3 1173 26 Travel_Rarely 1330 Research & Development 21
## 4 1174 55 Travel_Rarely 1311 Research & Development 2
## 5 1175 29 Travel_Rarely 1246 Sales 19
## Education EducationField EmployeeCount EmployeeNumber
## 1 3 Life Sciences 1 1596
## 2 3 Human Resources 1 1207
## 3 3 Medical 1 1107
## 4 3 Life Sciences 1 505
## 5 3 Life Sciences 1 1497
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 46 4 2
## 2 2 Male 99 3 1
## 3 1 Male 37 3 1
## 4 3 Female 97 3 4
## 5 3 Male 77 2 2
## JobRole JobSatisfaction MaritalStatus MonthlyIncome MonthlyRate
## 1 Laboratory Technician 3 Married 3407 25348
## 2 Human Resources 3 Married 3600 8429
## 3 Laboratory Technician 3 Divorced 2377 19373
## 4 Manager 4 Single 16659 23258
## 5 Sales Executive 3 Divorced 8620 23757
## NumCompaniesWorked Over18 OverTime PercentSalaryHike PerformanceRating
## 1 1 Y No 17 3
## 2 1 Y No 13 3
## 3 1 Y No 20 4
## 4 2 Y Yes 13 3
## 5 1 Y No 14 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 1 4 80 2 10
## 2 4 80 1 5
## 3 3 80 1 1
## 4 3 80 0 30
## 5 3 80 2 10
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 3 2 10 9
## 2 2 3 5 4
## 3 0 2 1 1
## 4 2 3 5 4
## 5 3 3 10 7
## YearsSinceLastPromotion YearsWithCurrManager
## 1 6 8
## 2 1 4
## 3 0 0
## 4 1 2
## 5 0 4#summary(AttritionTest)
vis_miss(AttritionTest)
Attritiondata <- subset(Attritiondata, select = -c(Over18))
head(Attritiondata,5)## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 13 4 Life Sciences 1 859
## 2 14 3 Medical 1 1128
## 3 18 2 Life Sciences 1 1412
## 4 1 4 Marketing 1 2016
## 5 2 1 Technical Degree 1 1646
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 73 3 2
## 2 3 Male 44 2 5
## 3 3 Male 60 3 3
## 4 3 Female 48 3 3
## 5 1 Female 32 3 1
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1 Sales Executive 4 Divorced 4403
## 2 Research Director 3 Single 19626
## 3 Manufacturing Director 4 Single 9362
## 4 Sales Executive 4 Married 10422
## 5 Research Scientist 4 Single 3760
## MonthlyRate NumCompaniesWorked OverTime PercentSalaryHike PerformanceRating
## 1 9250 2 No 11 3
## 2 17544 1 No 14 3
## 3 19944 2 No 11 3
## 4 24032 1 No 19 3
## 5 17218 1 Yes 13 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 1 3 80 1 8
## 2 1 80 0 21
## 3 3 80 0 10
## 4 3 80 2 14
## 5 3 80 0 6
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 3 2 5 2
## 2 2 4 20 7
## 3 2 3 2 2
## 4 3 3 14 10
## 5 2 3 6 3
## YearsSinceLastPromotion YearsWithCurrManager
## 1 0 3
## 2 4 9
## 3 2 2
## 4 5 7
## 5 1 3colnames(NoSalary)[colnames(NoSalary)=="ï..ID"] <- "ID"
colnames(NoSalary)## [1] "ID" "Age"
## [3] "Attrition" "BusinessTravel"
## [5] "DailyRate" "Department"
## [7] "DistanceFromHome" "Education"
## [9] "EducationField" "EmployeeCount"
## [11] "EmployeeNumber" "EnvironmentSatisfaction"
## [13] "Gender" "HourlyRate"
## [15] "JobInvolvement" "JobLevel"
## [17] "JobRole" "JobSatisfaction"
## [19] "MaritalStatus" "MonthlyRate"
## [21] "NumCompaniesWorked" "Over18"
## [23] "OverTime" "PercentSalaryHike"
## [25] "PerformanceRating" "RelationshipSatisfaction"
## [27] "StandardHours" "StockOptionLevel"
## [29] "TotalWorkingYears" "TrainingTimesLastYear"
## [31] "WorkLifeBalance" "YearsAtCompany"
## [33] "YearsInCurrentRole" "YearsSinceLastPromotion"
## [35] "YearsWithCurrManager"AttritionTest <- subset(AttritionTest, select = -c(Over18))
head(AttritionTest,5)## ID Age BusinessTravel DailyRate Department DistanceFromHome
## 1 1171 35 Travel_Rarely 750 Research & Development 28
## 2 1172 33 Travel_Rarely 147 Human Resources 2
## 3 1173 26 Travel_Rarely 1330 Research & Development 21
## 4 1174 55 Travel_Rarely 1311 Research & Development 2
## 5 1175 29 Travel_Rarely 1246 Sales 19
## Education EducationField EmployeeCount EmployeeNumber
## 1 3 Life Sciences 1 1596
## 2 3 Human Resources 1 1207
## 3 3 Medical 1 1107
## 4 3 Life Sciences 1 505
## 5 3 Life Sciences 1 1497
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 46 4 2
## 2 2 Male 99 3 1
## 3 1 Male 37 3 1
## 4 3 Female 97 3 4
## 5 3 Male 77 2 2
## JobRole JobSatisfaction MaritalStatus MonthlyIncome MonthlyRate
## 1 Laboratory Technician 3 Married 3407 25348
## 2 Human Resources 3 Married 3600 8429
## 3 Laboratory Technician 3 Divorced 2377 19373
## 4 Manager 4 Single 16659 23258
## 5 Sales Executive 3 Divorced 8620 23757
## NumCompaniesWorked OverTime PercentSalaryHike PerformanceRating
## 1 1 No 17 3
## 2 1 No 13 3
## 3 1 No 20 4
## 4 2 Yes 13 3
## 5 1 No 14 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 1 4 80 2 10
## 2 4 80 1 5
## 3 3 80 1 1
## 4 3 80 0 30
## 5 3 80 2 10
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 3 2 10 9
## 2 2 3 5 4
## 3 0 2 1 1
## 4 2 3 5 4
## 5 3 3 10 7
## YearsSinceLastPromotion YearsWithCurrManager
## 1 6 8
## 2 1 4
## 3 0 0
## 4 1 2
## 5 0 4EDA
Looking at relationships within overall data, not just those attrited
ggplot(data=Attritiondata, aes(x=JobSatisfaction)) +geom_bar(position="dodge") + theme_minimal() + ggtitle("Overall Job Satisfaction")
Here, we look at overall Job Satisfaction among the employees. It looks like majority of employees seem to be satisfied with their job, with a handful not being as satisfied. It is a left skewed histogram.
ggplot(data=Attritiondata,aes(x=MonthlyIncome, y=Age)) + geom_point(position="jitter") + facet_wrap(~MaritalStatus)+geom_smooth(method="loess") + ggtitle("Monthly Income and Age categorized by Marital Status")
Here, we see Monthly Income by Age categorized by Marital Status. Starting off, it looks like Divorced and Married people tend to make more as their age increases. However, the same positive trend is seen for Single people, but they do not make as much as their Married or Divorced coworkers. But there is definitely a positive trend between age and monthly income.
ggplot(data = Attritiondata, aes(x = MonthlyIncome, y = Age, color = JobInvolvement)) +
geom_point(position = "jitter") +
geom_smooth(method = lm) +
ggtitle("Job Involvement and Monthly Income") + facet_wrap(~JobInvolvement)
Looking at job involvement and monthly Income, it looks most employees regardless of Job Involvement have a positive correlation between Age and Monthly Income. I was trying to see if those who were more involved in their job made a higher income, but that does not seem to be the case at first glance. It looks like a lot of employees are pretty involved in their jobs (Job Involvement levels 2 and 3).
ggplot(data = Attritiondata, aes(x = MonthlyIncome, y = Age, color = interaction(JobInvolvement))) +
geom_point(position = "jitter") +
geom_smooth(method = lm, se = FALSE) +
ggtitle("Job Involvement and Monthly Income")
When I plotted all 4 job levels with their respective linear regression lines, it looks like those with least job involvement (Job Involvement 1) start to make more after crossing 40 years old and a monthly income of 10,000. It seems like Job Involvement 3 make the most starting off up to a monthly income of 10,000 and ~40 years old, and start to make the least as they near 50 years old. Job Involvements 2 and 4 linear regression lines fall in between the 1 and 4 lines initially and towards the end, they are very close to each otehr, and seem to overlap.
ggplot(data=Attritiondata,aes(x=MonthlyIncome,y=NumCompaniesWorked)) + geom_point(position="jitter") + geom_smooth(method=lm) + ggtitle("Number of Companies Worked and Monthly Income")
There is positive correlation between Monthly Income and Number of Companies worked.
ggplot(data = Attritiondata, aes(x = MonthlyIncome)) + geom_histogram() + ggtitle("Monthly Income Histogram")
Looking at a histogram of Monthly Income, it looks to be right skewed. The mode is less than the median which is less than the mean.
ggplot(data=Attritiondata,aes(x=MonthlyIncome)) + geom_histogram() + ggtitle("Monthly Income Histogram Categorized by Gender") + facet_wrap(~Gender)
Looking at monthly income categorized by Gender, it looks like there are more male datapoints in the dataset than femalses. The mode is higher for men than it is for women. Both histograms are right skewed.
ggplot(data=Attritiondata,aes(x=MonthlyIncome, y=DistanceFromHome)) + geom_point(position="jitter") + geom_smooth(method=loess) + ggtitle("Monthly Income and Distance from Home")
There seems to be a negative correlation between distance from home and monthly income. The plot and loess curve imply that as distance from home initially increases, monthly income also increases, but after a monthly income of 15,000 is exceeded, there is an overall decrease in the distance from home. The curve resembles a concave curve, with the downward slight ‘w’ shape. Looking at this graph, I would interpret distance from home to be in miles, because if this is in kilometers, it doesn’t seem to make logical sense.
ggplot(data=Attritiondata,aes(x=MonthlyIncome, y=DistanceFromHome)) + geom_point(position="jitter") + geom_smooth(method=loess) + facet_wrap(~Gender) + ggtitle("Monthly Income and Distance from Home categorized by Gender")
There seems to be a more prominent downturned ‘w’ shape for women than for men. But the overall relationship is as mentioned above (between distance from home and Monthly Income).
ggplot(data=Attritiondata, aes(x=Department, y=JobSatisfaction, color = Gender)) + geom_point(position ="jitter") + facet_wrap(~Gender) + ggtitle("Department and Job Satisfaction by Gender")
Job Satisfaction seems to be higher for Research and Development for both Males and Females. There are less data points for those in Human Resources so a clear defined relationship can’t be concluded. There seems to be decent job satisfaction for those in Sales too for both genders.
ggplot(data=Attritiondata,aes(x=Age, y=TotalWorkingYears, color = MonthlyIncome)) + geom_point(position = "jitter") + ggtitle("Age and Total Working Years with Monthly Income")
This plot confirms that as Age increases, total working years also increase, and monthly income seems to follow the same trend as well. There is a positive correlation between all 3 variables - Age, Total working years, and monthly income.
Attrition Specific Analysis
I filtered the data to look at specifically those who Attrited, to find some insights and relationships between the variables.
attrition_yes <- dplyr::filter(Attritiondata, Attrition == "Yes")ggplot(data = attrition_yes, aes(x = Department, fill = Gender))+ geom_bar(position = "dodge") +
ggtitle("Attrition by Department and Gender") + theme_minimal()
Of those who left the company, many men were in Research & Development and Sales, while there were equal amounts of women in Research & Development and Sales. There are equal amounts of men and women from Human resources who left the company.
ggplot(data = attrition_yes, aes(y = JobSatisfaction , x = DistanceFromHome, color = MonthlyIncome))+
geom_point(position = "jitter") + theme_minimal() + geom_smooth(method =lm) + ggtitle("Attrition by Distance from Home, Job Satisfaction with Monthly Income")
Of those who left the company, it seems likes a lot of them were making under $10,000 monthly. There seems to be a negative relationship between Job Satisfaction and Distance from home (as distance from home increases in miles, the job satisfaction goes down).
ggplot(data = attrition_yes, aes(y=JobLevel, x = MonthlyIncome, color = Age))+
geom_point(position="jitter")+geom_smooth(method=lm) + ggtitle("Monthly Income and Job Level with Age")
Of those who left the company, there is a positive relationship between job level and Monthly income. Age seems to be scattered, but at a job level of around 1, and monthly income less than 5000, the age group seems to have employees in their 20s.
ggplot(data = attrition_yes, aes(y=JobLevel, x = Age, color = Gender))+
geom_point(position="jitter")+geom_smooth(method=lm) + ggtitle("Attrition Job Level by Age and Gender")
OF those who left the company, it looks like Job Level is positively correlated with Age for both Males and Females. The slope of the linear regression line for females seems to be more steep than it is for the males.
ggplot(data = attrition_yes, aes(x=OverTime, fill = Gender)) + geom_bar() + ggtitle("Attrition - Overtime by Gender")
Of those who left the company, many employees were working over time. As mentioned earlier, there are more male data points compared to females, which is why at first glance it may seem a bit off. It looks like of the Over time group - approximately 70% were males, and 30% was females.
ggplot(data = attrition_yes, aes(x=NumCompaniesWorked, y = PercentSalaryHike)) + geom_point(position = "jitter") + theme_minimal()+geom_smooth(method = lm) + ggtitle ("Attrition - Percent Salary Hike and Number of Companies Worked")
There seems to be a slight downward trend when looking at the relationship between Number of companies worked and Percent salary hike. I was trying to see if the number of companies worked affected the percent salary hike positively. At first glance, it seems like there is no variation int he line, but if you observe closely, there is a slight downward trend.
ggplot(data = attrition_yes, aes(x = PercentSalaryHike, fill = OverTime)) +
geom_histogram( binwidth = 1, color = "black", alpha = 0.7) +
geom_density(aes(y = ..count..), fill = "transparent", color = "darkblue") +
labs(title = "Histogram with Trend Line of % Salary Hike by Overtime",
x = "Percent Salary Hike", y = "Count") +
theme_minimal() + theme(legend.position = "top") # Adjust legend position
This is a histogram of percent salary hike, with each bar being split and shaded by if the employee(s) were working over time. The overall percent salary hike (without the split) seems to be right skewed generally, but the shape of the line/trend seems to imply that it may be multimodal.
ggplot(data = attrition_yes, aes(x=YearsAtCompany, y = PercentSalaryHike, color = PerformanceRating)) + geom_point() + theme_minimal() + ggtitle("Salary Hike v. Years at Company with Performance Rating")
This plot takes a look at Years at Company and Percent salary hike. Those with a higher performance rating seem to have a higher percent salary hike. For a Percent salary hike less than 20%, it seems like the performance rating is under 4, and trends around the 3 rating.
ggplot(data = attrition_yes, aes(y=JobSatisfaction, x = Education)) + geom_point(position="jitter") + theme_minimal() + ggtitle("Job Satisfaction v. Education Categorized by Education Field") + facet_wrap(~EducationField) + geom_smooth(method = lm)
This plot looks at the relationship between Job Satisfaction based on Education categorized by education field. Most education fields seem to have a negative correlation between job satisfaction and years of education, except for the Medical field. The medical field is the only field where as education increases, the job satifaction also seems to increase.
#Building a Regression Model to Determine Salary
Regression Model 1 Using All Predictors to Determine Salary
Monthly Income is the “salary” variable
class(Attritiondata$MonthlyIncome)## [1] "integer"sum(is.na(Attritiondata$MonthlyIncome))## [1] 0summary(Attritiondata)## ID Age Attrition BusinessTravel
## Min. : 1.0 Min. :18.00 Length:870 Length:870
## 1st Qu.:218.2 1st Qu.:30.00 Class :character Class :character
## Median :435.5 Median :35.00 Mode :character Mode :character
## Mean :435.5 Mean :36.83
## 3rd Qu.:652.8 3rd Qu.:43.00
## Max. :870.0 Max. :60.00
## DailyRate Department DistanceFromHome Education
## Min. : 103.0 Length:870 Min. : 1.000 Min. :1.000
## 1st Qu.: 472.5 Class :character 1st Qu.: 2.000 1st Qu.:2.000
## Median : 817.5 Mode :character Median : 7.000 Median :3.000
## Mean : 815.2 Mean : 9.339 Mean :2.901
## 3rd Qu.:1165.8 3rd Qu.:14.000 3rd Qu.:4.000
## Max. :1499.0 Max. :29.000 Max. :5.000
## EducationField EmployeeCount EmployeeNumber EnvironmentSatisfaction
## Length:870 Min. :1 Min. : 1.0 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: 477.2 1st Qu.:2.000
## Mode :character Median :1 Median :1039.0 Median :3.000
## Mean :1 Mean :1029.8 Mean :2.701
## 3rd Qu.:1 3rd Qu.:1561.5 3rd Qu.:4.000
## Max. :1 Max. :2064.0 Max. :4.000
## Gender HourlyRate JobInvolvement JobLevel
## Length:870 Min. : 30.00 Min. :1.000 Min. :1.000
## Class :character 1st Qu.: 48.00 1st Qu.:2.000 1st Qu.:1.000
## Mode :character Median : 66.00 Median :3.000 Median :2.000
## Mean : 65.61 Mean :2.723 Mean :2.039
## 3rd Qu.: 83.00 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :100.00 Max. :4.000 Max. :5.000
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## Length:870 Min. :1.000 Length:870 Min. : 1081
## Class :character 1st Qu.:2.000 Class :character 1st Qu.: 2840
## Mode :character Median :3.000 Mode :character Median : 4946
## Mean :2.709 Mean : 6390
## 3rd Qu.:4.000 3rd Qu.: 8182
## Max. :4.000 Max. :19999
## MonthlyRate NumCompaniesWorked OverTime PercentSalaryHike
## Min. : 2094 Min. :0.000 Length:870 Min. :11.0
## 1st Qu.: 8092 1st Qu.:1.000 Class :character 1st Qu.:12.0
## Median :14074 Median :2.000 Mode :character Median :14.0
## Mean :14326 Mean :2.728 Mean :15.2
## 3rd Qu.:20456 3rd Qu.:4.000 3rd Qu.:18.0
## Max. :26997 Max. :9.000 Max. :25.0
## PerformanceRating RelationshipSatisfaction StandardHours StockOptionLevel
## Min. :3.000 Min. :1.000 Min. :80 Min. :0.0000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:80 1st Qu.:0.0000
## Median :3.000 Median :3.000 Median :80 Median :1.0000
## Mean :3.152 Mean :2.707 Mean :80 Mean :0.7839
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:80 3rd Qu.:1.0000
## Max. :4.000 Max. :4.000 Max. :80 Max. :3.0000
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Min. : 0.00 Min. :0.000 Min. :1.000 Min. : 0.000
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 3.000
## Median :10.00 Median :3.000 Median :3.000 Median : 5.000
## Mean :11.05 Mean :2.832 Mean :2.782 Mean : 6.962
## 3rd Qu.:15.00 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:10.000
## Max. :40.00 Max. :6.000 Max. :4.000 Max. :40.000
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 2.00
## Median : 3.000 Median : 1.000 Median : 3.00
## Mean : 4.205 Mean : 2.169 Mean : 4.14
## 3rd Qu.: 7.000 3rd Qu.: 3.000 3rd Qu.: 7.00
## Max. :18.000 Max. :15.000 Max. :17.00# Identify character variables
char_vars <- sapply(Attritiondata, is.character)
# Convert character variables to factors
Attritiondata[, char_vars] <- lapply(Attritiondata[, char_vars], as.factor)
#Check Factor Levels for Categorical variables:
sapply(Attritiondata[,char_vars],levels)## $Attrition
## [1] "No" "Yes"
##
## $BusinessTravel
## [1] "Non-Travel" "Travel_Frequently" "Travel_Rarely"
##
## $Department
## [1] "Human Resources" "Research & Development" "Sales"
##
## $EducationField
## [1] "Human Resources" "Life Sciences" "Marketing" "Medical"
## [5] "Other" "Technical Degree"
##
## $Gender
## [1] "Female" "Male"
##
## $JobRole
## [1] "Healthcare Representative" "Human Resources"
## [3] "Laboratory Technician" "Manager"
## [5] "Manufacturing Director" "Research Director"
## [7] "Research Scientist" "Sales Executive"
## [9] "Sales Representative"
##
## $MaritalStatus
## [1] "Divorced" "Married" "Single"
##
## $OverTime
## [1] "No" "Yes" #Noticed Over18 has only 1 factor level; so going to remove from dataset
# Attritiondata <- subset(Attritiondata, select = -c(Over18))
# Fit the linear regression model with all predictors
Model1_fit <- lm(MonthlyIncome ~ ., data = Attritiondata)
summary(Model1_fit)##
## Call:
## lm(formula = MonthlyIncome ~ ., data = Attritiondata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3708.8 -674.1 14.7 614.1 4100.0
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.013e+01 7.772e+02 0.077 0.938349
## ID -2.343e-01 1.476e-01 -1.588 0.112713
## Age -1.431e+00 5.649e+00 -0.253 0.800110
## AttritionYes 8.904e+01 1.154e+02 0.771 0.440729
## BusinessTravelTravel_Frequently 1.895e+02 1.420e+02 1.334 0.182441
## BusinessTravelTravel_Rarely 3.720e+02 1.200e+02 3.099 0.002005 **
## DailyRate 1.452e-01 9.129e-02 1.591 0.112062
## DepartmentResearch & Development 1.234e+02 4.768e+02 0.259 0.795866
## DepartmentSales -4.594e+02 4.877e+02 -0.942 0.346580
## DistanceFromHome -6.237e+00 4.578e+00 -1.362 0.173417
## Education -3.743e+01 3.716e+01 -1.007 0.314105
## EducationFieldLife Sciences 1.352e+02 3.692e+02 0.366 0.714248
## EducationFieldMarketing 1.377e+02 3.914e+02 0.352 0.725050
## EducationFieldMedical 3.326e+01 3.699e+02 0.090 0.928376
## EducationFieldOther 9.152e+01 3.946e+02 0.232 0.816664
## EducationFieldTechnical Degree 9.680e+01 3.843e+02 0.252 0.801179
## EmployeeCount NA NA NA NA
## EmployeeNumber 8.681e-02 6.103e-02 1.422 0.155269
## EnvironmentSatisfaction -6.267e+00 3.364e+01 -0.186 0.852252
## GenderMale 1.100e+02 7.442e+01 1.478 0.139715
## HourlyRate -3.591e-01 1.824e+00 -0.197 0.844003
## JobInvolvement 1.677e+01 5.321e+01 0.315 0.752698
## JobLevel 2.783e+03 8.340e+01 33.375 < 2e-16 ***
## JobRoleHuman Resources -2.053e+02 5.157e+02 -0.398 0.690663
## JobRoleLaboratory Technician -5.891e+02 1.714e+02 -3.437 0.000618 ***
## JobRoleManager 4.280e+03 2.830e+02 15.122 < 2e-16 ***
## JobRoleManufacturing Director 1.809e+02 1.696e+02 1.067 0.286497
## JobRoleResearch Director 4.077e+03 2.193e+02 18.592 < 2e-16 ***
## JobRoleResearch Scientist -3.494e+02 1.705e+02 -2.049 0.040790 *
## JobRoleSales Executive 5.263e+02 3.576e+02 1.472 0.141449
## JobRoleSales Representative 8.531e+01 3.918e+02 0.218 0.827703
## JobSatisfaction 3.278e+01 3.344e+01 0.980 0.327278
## MaritalStatusMarried 6.708e+01 1.002e+02 0.669 0.503497
## MaritalStatusSingle 1.128e+01 1.361e+02 0.083 0.933978
## MonthlyRate -9.505e-03 5.143e-03 -1.848 0.064946 .
## NumCompaniesWorked 5.421e+00 1.691e+01 0.321 0.748622
## OverTimeYes -1.394e+01 8.434e+01 -0.165 0.868787
## PercentSalaryHike 2.586e+01 1.581e+01 1.635 0.102351
## PerformanceRating -3.235e+02 1.614e+02 -2.004 0.045368 *
## RelationshipSatisfaction 1.640e+01 3.339e+01 0.491 0.623375
## StandardHours NA NA NA NA
## StockOptionLevel -2.758e+00 5.740e+01 -0.048 0.961695
## TotalWorkingYears 5.080e+01 1.098e+01 4.627 4.3e-06 ***
## TrainingTimesLastYear 2.436e+01 2.912e+01 0.837 0.403111
## WorkLifeBalance -3.472e+01 5.161e+01 -0.673 0.501284
## YearsAtCompany -2.750e+00 1.370e+01 -0.201 0.840925
## YearsInCurrentRole 3.398e+00 1.711e+01 0.199 0.842584
## YearsSinceLastPromotion 3.084e+01 1.532e+01 2.013 0.044405 *
## YearsWithCurrManager -2.691e+01 1.669e+01 -1.613 0.107210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1055 on 823 degrees of freedom
## Multiple R-squared: 0.9501, Adjusted R-squared: 0.9473
## F-statistic: 340.7 on 46 and 823 DF, p-value: < 2.2e-16#p val < alpha of .05, it affects the Salary Variable
#Model1_Preds = predict(Model1_fit, newdata = NoSalary) #this is an example of predict function you would want to use
#as.data.frame(Model1_Preds)
#write.csv(Model1_Preds,"Model1PredictionsNoSalaryRenuKarthikeyan.csv")Looking at this summary of model 1 output, it indicates that the statistically significant p values are Business Travel, JobLevel, Job Role, Performance rating, Total working Years, and Years since last promotion. The F-statistic tests the overall significance of the model. The F-statistic is 340.7 with a very small p-value (< 2.2e-16), suggests that at least one predictor variable is significantly related to Monthly Income.There are two coefficients not defined because of singularities. This might indicate multicollinearity, where two or more predictor variables are highly correlated.
Linear Regression using Select predictors to Determine Salary
Model2_fit = lm(MonthlyIncome ~ NumCompaniesWorked + Age + Gender + MaritalStatus + JobInvolvement + JobRole + DistanceFromHome + JobLevel + Education, data = Attritiondata)
summary(Model2_fit) # P value overall implies that at least one of my variables' slope != 0. ##
## Call:
## lm(formula = MonthlyIncome ~ NumCompaniesWorked + Age + Gender +
## MaritalStatus + JobInvolvement + JobRole + DistanceFromHome +
## JobLevel + Education, data = Attritiondata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3536.4 -699.4 -39.1 659.8 4178.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -600.926 320.383 -1.876 0.06105 .
## NumCompaniesWorked 17.866 15.366 1.163 0.24526
## Age 12.875 4.962 2.595 0.00963 **
## GenderMale 120.277 75.308 1.597 0.11061
## MaritalStatusMarried 106.159 95.406 1.113 0.26615
## MaritalStatusSingle 23.451 103.944 0.226 0.82155
## JobInvolvement 15.469 52.613 0.294 0.76882
## JobRoleHuman Resources -327.849 255.950 -1.281 0.20057
## JobRoleLaboratory Technician -534.026 172.285 -3.100 0.00200 **
## JobRoleManager 3933.510 232.847 16.893 < 2e-16 ***
## JobRoleManufacturing Director 92.825 169.877 0.546 0.58492
## JobRoleResearch Director 3919.755 218.518 17.938 < 2e-16 ***
## JobRoleResearch Scientist -246.097 171.976 -1.431 0.15280
## JobRoleSales Executive -122.894 146.575 -0.838 0.40202
## JobRoleSales Representative -392.881 217.075 -1.810 0.07067 .
## DistanceFromHome -7.908 4.553 -1.737 0.08281 .
## JobLevel 3042.789 69.854 43.559 < 2e-16 ***
## Education -33.713 37.379 -0.902 0.36736
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1078 on 852 degrees of freedom
## Multiple R-squared: 0.9461, Adjusted R-squared: 0.945
## F-statistic: 878.9 on 17 and 852 DF, p-value: < 2.2e-16NoSalary$MonthlyIncome = predict(Model2_fit,newdata = NoSalary)
#Model2_Preds<- NoSalary %>% select(c("ID","MonthlyIncome"))
#as.data.frame(Model2_Preds)
#write.csv(Model2_Preds,"Model2PredictionsNoSalaryRenuKarthikeyan.csv", row.names = T)These select predictors were chosen because of the insights from the EDA. I thought they were significant predictors. The coefficient for JobLevel is 3042.789. This suggests that, on average, an increase of one unit in JobLevel is associated with an increase of 3042.789 dollars in MonthlyIncome.Likewise, Age has a coefficient of 12.875 indicating that a year increase in age, results in 12.88 dollars additional monthly income, holding all other variables constant. Males have a coefficient of 120, indicating that holding all other variables constant, males make an additional 120 dollars compared to women monthly. The coefficient for distance from home has a. -7.908, which indicates each additional mile away from home may result in a monthly salary decrease by -7 dollars.
The statistically significant p values (<.10) are Age, certain Job Roles – like Laboratory Technician, Manager, and Research director, Job Level;
Overall, Model 2 appears to have a high R-squared value, indicating a good fit to the data. Many predictors are statistically significant, suggesting they contribute to determining Monthly Income
Split and Train Linear Regression Models to Predict Salary, with average of cross validation
set.seed(1234)
TrainObs = sample(seq(1,dim(Attritiondata)[1]),round(.8*dim(Attritiondata)[1]),replace = FALSE)
SalaryTrain = Attritiondata[TrainObs,]
SalaryTrain## ID Age Attrition BusinessTravel DailyRate Department
## 284 284 31 No Travel_Rarely 691 Sales
## 848 848 39 No Travel_Rarely 1132 Research & Development
## 101 101 27 No Travel_Rarely 1377 Research & Development
## 623 623 34 No Travel_Rarely 182 Research & Development
## 645 645 41 Yes Non-Travel 906 Research & Development
## 400 400 44 No Travel_Frequently 1193 Research & Development
## 98 98 37 No Non-Travel 1040 Research & Development
## 103 103 34 No Non-Travel 1381 Sales
## 726 726 25 No Travel_Rarely 685 Research & Development
## 602 602 32 No Non-Travel 1200 Research & Development
## 326 326 27 No Travel_Rarely 618 Research & Development
## 79 79 45 No Non-Travel 1052 Sales
## 270 270 30 No Travel_Rarely 1176 Research & Development
## 382 382 26 No Travel_Rarely 1355 Human Resources
## 184 184 39 No Travel_Rarely 1089 Research & Development
## 574 574 34 No Travel_Rarely 810 Sales
## 4 4 32 No Travel_Rarely 801 Sales
## 661 661 32 No Travel_Frequently 430 Research & Development
## 552 552 28 No Travel_Rarely 1169 Human Resources
## 212 212 42 No Travel_Rarely 265 Sales
## 195 195 26 No Travel_Rarely 683 Research & Development
## 511 511 38 No Travel_Rarely 345 Sales
## 479 479 52 No Travel_Rarely 258 Research & Development
## 605 605 29 Yes Travel_Rarely 428 Sales
## 634 634 36 No Travel_Rarely 922 Research & Development
## 578 578 44 No Non-Travel 981 Research & Development
## 510 510 41 Yes Travel_Rarely 1360 Research & Development
## 687 687 29 Yes Travel_Frequently 337 Research & Development
## 424 424 18 No Non-Travel 1124 Research & Development
## 379 379 29 No Travel_Rarely 942 Research & Development
## 816 816 33 No Travel_Rarely 117 Research & Development
## 108 108 55 No Travel_Rarely 189 Human Resources
## 131 131 29 No Travel_Rarely 991 Sales
## 343 343 33 No Travel_Rarely 654 Research & Development
## 41 41 58 No Non-Travel 350 Sales
## 627 627 24 No Travel_Rarely 1476 Sales
## 740 740 35 No Travel_Rarely 538 Research & Development
## 298 298 34 Yes Travel_Frequently 658 Research & Development
## 811 811 30 No Non-Travel 111 Research & Development
## 258 258 30 No Travel_Rarely 855 Sales
## 629 629 55 No Travel_Frequently 1091 Research & Development
## 859 859 41 No Travel_Rarely 933 Research & Development
## 182 182 38 No Travel_Rarely 1009 Sales
## 305 305 58 Yes Travel_Rarely 289 Research & Development
## 358 358 29 Yes Travel_Rarely 408 Research & Development
## 696 696 47 No Travel_Rarely 1454 Sales
## 307 307 50 No Travel_Rarely 804 Research & Development
## 827 827 31 Yes Travel_Rarely 1079 Sales
## 221 221 34 No Travel_Rarely 304 Sales
## 736 736 27 No Travel_Rarely 511 Sales
## 561 561 36 No Travel_Frequently 1480 Research & Development
## 313 313 31 Yes Travel_Frequently 561 Research & Development
## 136 136 40 No Travel_Frequently 791 Research & Development
## 794 794 36 No Travel_Rarely 1223 Research & Development
## 145 145 40 No Travel_Rarely 1202 Research & Development
## 123 123 45 No Travel_Rarely 930 Sales
## 776 776 23 No Travel_Rarely 310 Research & Development
## 234 234 54 No Travel_Rarely 1147 Sales
## 608 608 38 No Travel_Rarely 322 Sales
## 495 495 36 No Travel_Rarely 1266 Sales
## 534 534 32 No Non-Travel 1146 Research & Development
## 803 803 39 No Travel_Rarely 117 Research & Development
## 297 297 34 No Travel_Frequently 1069 Research & Development
## 208 208 38 Yes Travel_Rarely 1180 Research & Development
## 838 838 34 No Travel_Rarely 258 Sales
## 569 569 42 No Travel_Rarely 462 Sales
## 522 522 28 Yes Travel_Rarely 329 Research & Development
## 248 248 32 No Travel_Rarely 234 Sales
## 365 365 55 No Travel_Rarely 282 Research & Development
## 665 665 34 No Non-Travel 1065 Sales
## 643 643 55 No Non-Travel 444 Research & Development
## 595 595 40 No Travel_Rarely 884 Research & Development
## 434 434 29 No Travel_Rarely 590 Research & Development
## 757 757 35 No Travel_Rarely 1137 Research & Development
## 760 760 45 Yes Travel_Rarely 1449 Sales
## 218 218 48 No Travel_Frequently 117 Research & Development
## 727 727 37 No Travel_Frequently 1231 Sales
## 508 508 51 No Travel_Frequently 541 Sales
## 276 276 56 No Travel_Rarely 718 Research & Development
## 169 169 52 No Travel_Rarely 956 Research & Development
## 71 71 41 No Travel_Rarely 642 Research & Development
## 573 573 28 No Travel_Frequently 783 Sales
## 860 860 44 Yes Travel_Rarely 621 Research & Development
## 485 485 58 Yes Travel_Rarely 147 Research & Development
## 667 667 29 No Travel_Frequently 1413 Sales
## 460 460 36 No Travel_Rarely 172 Research & Development
## 60 60 25 Yes Travel_Frequently 599 Sales
## 449 449 38 No Travel_Frequently 653 Research & Development
## 548 548 40 No Travel_Frequently 720 Research & Development
## 19 19 34 No Travel_Rarely 181 Research & Development
## 649 649 35 No Travel_Rarely 776 Sales
## 638 638 34 No Travel_Rarely 1239 Sales
## 670 670 54 No Travel_Rarely 685 Research & Development
## 319 319 39 No Travel_Rarely 1498 Sales
## 116 116 37 No Non-Travel 1252 Sales
## 840 840 48 No Travel_Rarely 1224 Research & Development
## 102 102 31 No Travel_Rarely 326 Sales
## 214 214 42 No Travel_Rarely 932 Research & Development
## 390 390 21 Yes Travel_Frequently 756 Sales
## 597 597 35 No Travel_Rarely 672 Research & Development
## 771 771 33 No Travel_Frequently 508 Sales
## 160 160 42 No Travel_Frequently 458 Research & Development
## 77 77 34 No Travel_Rarely 971 Sales
## 529 529 35 No Travel_Rarely 1214 Research & Development
## 126 126 35 No Travel_Rarely 1224 Sales
## 262 262 29 Yes Travel_Rarely 224 Research & Development
## 442 442 29 No Travel_Rarely 1082 Research & Development
## 642 642 40 No Travel_Rarely 750 Research & Development
## 181 181 35 No Travel_Rarely 819 Research & Development
## 163 163 33 No Travel_Rarely 1216 Sales
## 474 474 45 No Travel_Rarely 1316 Research & Development
## 228 228 22 No Travel_Rarely 1136 Research & Development
## 774 774 36 Yes Travel_Rarely 318 Research & Development
## 646 646 40 No Travel_Rarely 630 Sales
## 265 265 24 No Travel_Rarely 1127 Research & Development
## 427 427 30 No Non-Travel 990 Research & Development
## 781 781 41 Yes Travel_Rarely 1356 Sales
## 249 249 41 No Travel_Frequently 1200 Research & Development
## 810 810 27 No Travel_Rarely 1157 Research & Development
## 40 40 31 No Travel_Frequently 793 Sales
## 541 541 30 No Travel_Rarely 853 Research & Development
## 497 497 45 No Travel_Rarely 192 Research & Development
## 697 697 48 No Travel_Frequently 365 Research & Development
## 865 865 45 No Travel_Rarely 1448 Research & Development
## 450 450 45 No Non-Travel 336 Sales
## 600 600 24 Yes Travel_Rarely 813 Research & Development
## 778 778 32 No Non-Travel 862 Sales
## 363 363 52 Yes Travel_Rarely 266 Sales
## 478 478 26 No Travel_Rarely 775 Sales
## 403 403 47 No Travel_Rarely 207 Research & Development
## 375 375 20 No Travel_Rarely 727 Sales
## 335 335 59 No Non-Travel 1420 Human Resources
## 598 598 40 No Travel_Rarely 555 Research & Development
## 142 142 58 No Travel_Rarely 848 Research & Development
## 444 444 44 No Travel_Rarely 528 Human Resources
## 741 741 25 No Travel_Rarely 959 Sales
## 659 659 41 No Travel_Rarely 483 Research & Development
## 790 790 25 No Travel_Rarely 266 Research & Development
## 457 457 31 No Travel_Rarely 1112 Sales
## 188 188 47 No Travel_Rarely 1225 Sales
## 432 432 40 No Non-Travel 458 Research & Development
## 488 488 31 No Travel_Rarely 670 Research & Development
## 538 538 42 No Travel_Rarely 544 Human Resources
## 752 752 31 Yes Travel_Frequently 1060 Sales
## 215 215 27 No Travel_Rarely 1115 Research & Development
## 540 540 42 No Travel_Rarely 647 Sales
## 613 613 33 Yes Travel_Rarely 465 Research & Development
## 730 730 22 No Travel_Rarely 594 Research & Development
## 296 296 35 No Travel_Rarely 1490 Research & Development
## 328 328 42 No Travel_Rarely 557 Research & Development
## 147 147 33 No Travel_Rarely 931 Research & Development
## 701 701 29 No Travel_Rarely 352 Human Resources
## 708 708 31 No Travel_Rarely 1398 Human Resources
## 84 84 37 No Travel_Rarely 1319 Research & Development
## 83 83 38 No Travel_Frequently 508 Research & Development
## 250 250 41 No Travel_Rarely 1276 Sales
## 767 767 41 No Travel_Rarely 509 Research & Development
## 281 281 43 No Travel_Rarely 1179 Sales
## 675 675 27 No Travel_Rarely 486 Research & Development
## 843 843 52 No Travel_Frequently 890 Research & Development
## 431 431 27 No Travel_Rarely 1377 Sales
## 30 30 52 No Non-Travel 715 Research & Development
## 10 10 34 No Travel_Frequently 653 Research & Development
## 817 817 30 No Travel_Rarely 946 Research & Development
## 441 441 32 No Travel_Frequently 1316 Research & Development
## 820 820 28 Yes Travel_Rarely 1475 Sales
## 345 345 30 No Travel_Rarely 1329 Sales
## 592 592 34 Yes Travel_Rarely 790 Sales
## 585 585 44 No Travel_Rarely 1459 Research & Development
## 660 660 36 No Travel_Frequently 1195 Research & Development
## 12 12 28 No Non-Travel 280 Human Resources
## 293 293 27 No Travel_Rarely 894 Research & Development
## 303 303 55 No Travel_Rarely 1441 Research & Development
## 738 738 59 No Travel_Rarely 326 Sales
## 678 678 40 No Travel_Frequently 692 Research & Development
## 338 338 34 No Travel_Frequently 618 Research & Development
## 350 350 56 No Travel_Rarely 832 Research & Development
## 720 720 28 No Travel_Rarely 950 Research & Development
## 658 658 54 No Travel_Frequently 1050 Research & Development
## 107 107 29 No Non-Travel 1496 Research & Development
## 543 543 27 No Non-Travel 1450 Research & Development
## 518 518 42 No Travel_Rarely 855 Research & Development
## 854 854 28 No Travel_Rarely 1476 Research & Development
## 43 43 49 No Travel_Rarely 464 Research & Development
## 189 189 50 No Travel_Rarely 797 Research & Development
## 171 171 30 No Travel_Rarely 501 Sales
## 39 39 56 No Travel_Rarely 206 Human Resources
## 216 216 40 No Travel_Rarely 896 Research & Development
## 291 291 45 No Travel_Rarely 974 Research & Development
## 58 58 29 No Travel_Rarely 1370 Research & Development
## 395 395 42 No Travel_Frequently 1271 Research & Development
## 856 856 35 No Travel_Rarely 660 Sales
## 456 456 40 No Travel_Rarely 611 Sales
## 22 22 48 No Travel_Rarely 817 Sales
## 170 170 29 No Travel_Rarely 1107 Research & Development
## 588 588 36 Yes Travel_Rarely 1218 Sales
## 63 63 31 No Travel_Rarely 1154 Sales
## 676 676 46 No Travel_Rarely 168 Sales
## 719 719 35 Yes Travel_Frequently 880 Sales
## 417 417 30 No Non-Travel 1116 Research & Development
## 789 789 55 No Travel_Rarely 478 Research & Development
## 70 70 37 No Travel_Rarely 1372 Research & Development
## 59 59 37 No Non-Travel 728 Research & Development
## 800 800 36 No Travel_Rarely 1040 Research & Development
## 484 484 56 No Travel_Rarely 713 Research & Development
## 203 203 32 No Travel_Rarely 859 Research & Development
## 227 227 35 No Travel_Rarely 464 Research & Development
## 243 243 29 No Travel_Rarely 1328 Research & Development
## 413 413 37 No Travel_Rarely 408 Research & Development
## 577 577 38 No Travel_Rarely 1404 Sales
## 542 542 33 No Travel_Rarely 867 Research & Development
## 476 476 46 No Travel_Frequently 1211 Sales
## 744 744 23 No Travel_Rarely 160 Research & Development
## 804 804 35 Yes Travel_Rarely 1204 Sales
## 405 405 35 No Travel_Rarely 1343 Research & Development
## 397 397 46 No Travel_Rarely 1009 Research & Development
## 607 607 59 No Travel_Frequently 1225 Sales
## 501 501 58 Yes Travel_Rarely 601 Research & Development
## 429 429 32 Yes Travel_Rarely 515 Research & Development
## 205 205 32 No Travel_Rarely 1401 Sales
## 475 475 28 No Travel_Rarely 895 Research & Development
## 104 104 23 No Travel_Rarely 885 Research & Development
## 210 210 41 No Travel_Rarely 802 Sales
## 471 471 27 No Travel_Rarely 1354 Research & Development
## 66 66 32 No Travel_Rarely 334 Research & Development
## 88 88 35 Yes Travel_Rarely 622 Research & Development
## 826 826 46 No Travel_Rarely 430 Research & Development
## 601 601 24 No Non-Travel 830 Sales
## 309 309 55 No Travel_Frequently 135 Research & Development
## 737 737 39 No Travel_Rarely 722 Sales
## 294 294 47 No Non-Travel 1162 Research & Development
## 421 421 34 No Travel_Rarely 1153 Research & Development
## 769 769 46 No Travel_Rarely 150 Research & Development
## 430 430 34 No Travel_Rarely 470 Research & Development
## 512 512 39 No Non-Travel 1485 Research & Development
## 361 361 52 No Travel_Rarely 699 Research & Development
## 814 814 47 No Travel_Rarely 1176 Human Resources
## 480 480 37 No Travel_Rarely 161 Research & Development
## 15 15 46 No Travel_Rarely 991 Human Resources
## 486 486 41 Yes Travel_Rarely 1102 Sales
## 254 254 31 Yes Travel_Rarely 359 Human Resources
## 491 491 49 No Travel_Frequently 636 Research & Development
## 713 713 48 No Travel_Rarely 969 Research & Development
## 178 178 26 Yes Travel_Frequently 342 Research & Development
## 428 428 21 No Travel_Rarely 996 Research & Development
## 691 691 38 No Travel_Frequently 888 Human Resources
## 194 194 48 No Travel_Rarely 163 Sales
## 599 599 27 No Travel_Rarely 155 Research & Development
## 802 802 31 No Travel_Rarely 828 Sales
## 378 378 42 No Travel_Rarely 419 Sales
## 639 639 24 No Travel_Rarely 691 Research & Development
## 155 155 34 No Travel_Rarely 546 Research & Development
## 166 166 20 Yes Travel_Rarely 500 Sales
## 207 207 43 Yes Travel_Rarely 1372 Sales
## 468 468 36 No Travel_Rarely 132 Research & Development
## 105 105 53 No Travel_Rarely 447 Research & Development
## 482 482 31 No Travel_Rarely 1222 Research & Development
## 587 587 38 No Non-Travel 573 Research & Development
## 280 280 55 No Travel_Rarely 1229 Research & Development
## 440 440 38 No Travel_Rarely 827 Research & Development
## 389 389 36 No Travel_Rarely 530 Sales
## 499 499 36 No Travel_Rarely 1299 Research & Development
## 251 251 33 No Non-Travel 1313 Research & Development
## 231 231 45 No Travel_Rarely 1329 Research & Development
## 564 564 31 No Non-Travel 697 Research & Development
## 640 640 27 Yes Travel_Rarely 1420 Sales
## 463 463 50 No Travel_Frequently 809 Sales
## 100 100 34 No Travel_Rarely 401 Research & Development
## 731 731 43 No Travel_Rarely 177 Research & Development
## 141 141 56 No Travel_Rarely 1369 Research & Development
## 841 841 18 Yes Travel_Rarely 230 Research & Development
## 745 745 25 No Travel_Rarely 1382 Sales
## 36 36 32 No Travel_Frequently 1311 Research & Development
## 792 792 34 No Travel_Rarely 629 Research & Development
## 362 362 29 Yes Travel_Frequently 746 Sales
## 86 86 59 No Travel_Rarely 715 Research & Development
## 138 138 54 No Travel_Rarely 1441 Research & Development
## 344 344 30 No Travel_Frequently 160 Research & Development
## 211 211 41 No Travel_Rarely 582 Research & Development
## 461 461 42 No Travel_Frequently 1474 Research & Development
## 591 591 22 No Non-Travel 457 Research & Development
## 829 829 38 No Travel_Frequently 594 Research & Development
## 672 672 33 No Travel_Rarely 392 Sales
## 653 653 40 No Travel_Frequently 593 Research & Development
## 832 832 19 Yes Travel_Rarely 528 Sales
## 371 371 31 No Travel_Frequently 853 Research & Development
## 380 380 30 Yes Travel_Rarely 138 Research & Development
## 97 97 46 No Travel_Rarely 228 Sales
## 49 49 18 Yes Travel_Frequently 1306 Sales
## 469 469 26 Yes Non-Travel 265 Sales
## 260 260 25 No Travel_Rarely 977 Research & Development
## 650 650 40 Yes Travel_Rarely 1329 Research & Development
## 174 174 33 No Travel_Rarely 536 Sales
## 775 775 42 No Travel_Frequently 288 Research & Development
## 554 554 48 No Travel_Rarely 1469 Research & Development
## 723 723 34 No Travel_Frequently 560 Research & Development
## 183 183 43 No Travel_Frequently 957 Research & Development
## 347 347 34 No Travel_Frequently 1003 Research & Development
## 74 74 41 No Travel_Rarely 167 Research & Development
## 641 641 30 No Travel_Rarely 1358 Research & Development
## 631 631 55 No Travel_Rarely 836 Research & Development
## 118 118 29 No Travel_Rarely 1378 Research & Development
## 271 271 45 No Non-Travel 589 Sales
## 551 551 41 No Travel_Rarely 314 Human Resources
## 763 763 41 No Non-Travel 256 Sales
## 357 357 26 No Travel_Rarely 841 Research & Development
## 252 252 36 No Non-Travel 1229 Sales
## 619 619 39 No Travel_Rarely 613 Research & Development
## 566 566 44 No Travel_Rarely 1037 Research & Development
## 459 459 56 No Travel_Rarely 1255 Research & Development
## 61 61 36 Yes Travel_Rarely 530 Sales
## 179 179 32 No Travel_Frequently 967 Sales
## 272 272 34 No Travel_Rarely 1130 Research & Development
## 76 76 30 No Travel_Frequently 721 Research & Development
## 87 87 47 No Travel_Rarely 703 Sales
## 224 224 28 No Travel_Rarely 1423 Research & Development
## 739 739 35 No Travel_Rarely 1219 Sales
## 164 164 42 No Travel_Rarely 916 Research & Development
## 13 13 35 No Travel_Rarely 950 Research & Development
## 526 526 53 No Travel_Frequently 124 Sales
## 836 836 37 No Non-Travel 142 Sales
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## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
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## 848 5 2 18 10
## 101 3 4 5 0
## 623 2 3 4 2
## 645 2 1 1 0
## 400 2 2 2 2
## 98 2 4 1 0
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## 8 8 37 No Travel_Rarely 309 Sales
## 9 9 34 No Travel_Rarely 1333 Sales
## 16 16 31 No Non-Travel 1188 Sales
## 18 18 46 No Non-Travel 1144 Research & Development
## 20 20 44 No Travel_Rarely 170 Research & Development
## 26 26 44 No Travel_Rarely 1099 Sales
## 44 44 42 No Travel_Rarely 1059 Research & Development
## 46 46 42 No Travel_Frequently 748 Research & Development
## 47 47 32 Yes Travel_Rarely 964 Sales
## 50 50 27 No Non-Travel 691 Research & Development
## 52 52 58 No Travel_Rarely 1055 Research & Development
## 56 56 41 No Travel_Rarely 548 Research & Development
## 62 62 36 No Travel_Rarely 325 Research & Development
## 69 69 35 No Travel_Frequently 664 Research & Development
## 72 72 28 No Travel_Rarely 866 Sales
## 73 73 35 Yes Travel_Rarely 556 Research & Development
## 78 78 46 No Travel_Rarely 563 Sales
## 80 80 31 No Travel_Rarely 1276 Research & Development
## 81 81 37 Yes Travel_Rarely 1141 Research & Development
## 89 89 27 No Travel_Frequently 994 Sales
## 90 90 46 No Travel_Rarely 705 Sales
## 92 92 29 Yes Travel_Rarely 408 Sales
## 93 93 37 Yes Travel_Rarely 1373 Research & Development
## 96 96 42 No Travel_Rarely 635 Sales
## 99 99 29 No Travel_Frequently 410 Research & Development
## 109 109 46 Yes Travel_Rarely 261 Research & Development
## 111 111 28 No Travel_Frequently 773 Research & Development
## 115 115 35 No Travel_Rarely 1361 Sales
## 117 117 25 No Travel_Rarely 1356 Sales
## 130 130 46 No Travel_Rarely 1319 Sales
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## 137 137 29 Yes Travel_Rarely 805 Research & Development
## 139 139 39 Yes Travel_Rarely 1122 Research & Development
## 140 140 37 No Travel_Rarely 1189 Sales
## 144 144 37 No Travel_Rarely 228 Sales
## 148 148 33 No Travel_Rarely 1242 Sales
## 151 151 31 Yes Travel_Rarely 1365 Sales
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## 167 167 49 No Travel_Rarely 301 Research & Development
## 172 172 47 No Non-Travel 1169 Research & Development
## 173 173 46 No Travel_Frequently 1034 Research & Development
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## 180 180 38 No Travel_Rarely 702 Sales
## 193 193 46 No Travel_Rarely 1485 Research & Development
## 196 196 51 No Travel_Rarely 833 Research & Development
## 197 197 51 No Travel_Rarely 432 Research & Development
## 200 200 24 No Travel_Rarely 823 Research & Development
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## 209 209 30 No Travel_Rarely 201 Research & Development
## 213 213 37 No Travel_Rarely 1225 Research & Development
## 220 220 26 No Travel_Rarely 583 Research & Development
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## 240 240 50 No Travel_Rarely 813 Research & Development
## 244 244 25 Yes Travel_Rarely 383 Sales
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## 261 261 31 Yes Travel_Frequently 703 Sales
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## 339 339 50 Yes Travel_Frequently 959 Sales
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## 603 603 30 No Travel_Rarely 153 Research & Development
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## 611 611 35 No Travel_Rarely 817 Research & Development
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## 614 614 19 Yes Travel_Rarely 419 Sales
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## 626 626 27 No Travel_Rarely 1167 Research & Development
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## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
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## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 5 Research Scientist 4 Single 3760
## 8 Sales Executive 4 Divorced 6694
## 9 Sales Representative 3 Married 2220
## 16 Sales Executive 3 Married 6932
## 18 Manufacturing Director 3 Married 5258
## 20 Healthcare Representative 1 Married 5033
## 26 Manager 2 Married 18213
## 44 Manager 4 Single 19613
## 46 Laboratory Technician 4 Single 3673
## 47 Sales Executive 2 Single 6735
## 50 Research Scientist 2 Divorced 2024
## 52 Research Director 1 Married 19701
## 56 Laboratory Technician 1 Divorced 2289
## 62 Healthcare Representative 3 Married 7094
## 69 Research Scientist 1 Married 2194
## 72 Sales Executive 1 Single 8463
## 73 Manufacturing Director 3 Married 5916
## 78 Manager 1 Single 17567
## 80 Laboratory Technician 4 Divorced 1129
## 81 Healthcare Representative 2 Married 4777
## 89 Sales Executive 3 Single 8726
## 90 Manager 1 Single 18947
## 92 Sales Executive 1 Married 7336
## 93 Laboratory Technician 3 Single 2090
## 96 Sales Executive 3 Married 4907
## 99 Laboratory Technician 2 Married 3180
## 109 Healthcare Representative 2 Married 8926
## 111 Research Scientist 3 Divorced 2703
## 115 Sales Executive 1 Married 8966
## 117 Sales Executive 4 Single 4950
## 130 Sales Executive 1 Divorced 13225
## 134 Manufacturing Director 2 Married 5295
## 137 Laboratory Technician 1 Married 2319
## 139 Laboratory Technician 1 Married 2404
## 140 Sales Executive 4 Single 7428
## 144 Sales Executive 4 Married 6502
## 148 Sales Executive 1 Married 6392
## 151 Sales Executive 1 Divorced 4233
## 158 Healthcare Representative 4 Divorced 12169
## 167 Research Director 2 Married 16413
## 172 Research Scientist 2 Married 5467
## 173 Healthcare Representative 3 Married 10527
## 175 Manager 3 Divorced 14852
## 180 Sales Executive 4 Single 8686
## 193 Manufacturing Director 3 Divorced 4810
## 196 Research Scientist 4 Married 2723
## 197 Laboratory Technician 4 Married 2075
## 200 Laboratory Technician 2 Married 2127
## 202 Research Scientist 2 Married 6220
## 209 Research Scientist 1 Divorced 3204
## 213 Research Scientist 4 Single 4680
## 220 Research Scientist 4 Single 2875
## 233 Sales Executive 3 Married 12936
## 236 Sales Executive 3 Married 10609
## 239 Human Resources 2 Married 2187
## 240 Research Director 1 Divorced 13269
## 244 Sales Representative 1 Married 4400
## 246 Laboratory Technician 4 Divorced 2532
## 257 Sales Representative 2 Divorced 2789
## 261 Sales Representative 4 Single 2785
## 266 Laboratory Technician 2 Married 2426
## 268 Laboratory Technician 2 Single 1393
## 273 Sales Representative 3 Married 3540
## 282 Laboratory Technician 1 Single 4721
## 283 Healthcare Representative 4 Divorced 6513
## 288 Laboratory Technician 4 Divorced 2661
## 301 Sales Executive 2 Married 6524
## 304 Research Scientist 3 Single 5098
## 312 Manufacturing Director 3 Single 13237
## 315 Sales Executive 1 Single 5405
## 316 Sales Executive 4 Single 4163
## 322 Research Scientist 4 Married 2691
## 323 Research Scientist 4 Divorced 3761
## 327 Research Director 4 Divorced 19049
## 336 Research Scientist 3 Single 4508
## 337 Sales Executive 4 Divorced 4968
## 339 Sales Executive 3 Single 4728
## 342 Laboratory Technician 4 Married 3867
## 355 Sales Executive 3 Divorced 10447
## 356 Sales Executive 1 Single 4759
## 359 Manufacturing Director 4 Married 6162
## 366 Sales Executive 1 Married 13212
## 370 Research Scientist 4 Single 5055
## 374 Research Scientist 2 Married 3936
## 376 Research Director 4 Single 14411
## 386 Research Director 1 Married 13582
## 387 Sales Executive 3 Single 8789
## 388 Manufacturing Director 4 Divorced 4262
## 396 Research Scientist 4 Divorced 2695
## 398 Human Resources 3 Married 2742
## 402 Manufacturing Director 3 Divorced 5265
## 406 Research Director 4 Divorced 16627
## 410 Research Director 1 Married 17159
## 426 Sales Executive 4 Divorced 10798
## 447 Research Scientist 4 Married 4735
## 452 Research Scientist 2 Single 3420
## 458 Sales Executive 4 Single 5441
## 462 Laboratory Technician 2 Single 2013
## 466 Research Scientist 4 Single 3944
## 473 Research Director 1 Married 13245
## 483 Research Scientist 4 Married 5747
## 487 Sales Representative 4 Single 1081
## 493 Sales Executive 3 Single 5869
## 502 Laboratory Technician 3 Single 2028
## 505 Research Director 3 Married 17169
## 514 Research Director 4 Single 13458
## 515 Sales Representative 4 Single 2610
## 521 Research Scientist 4 Single 2553
## 527 Sales Executive 1 Divorced 5006
## 531 Healthcare Representative 3 Married 8823
## 533 Research Scientist 3 Married 2700
## 536 Research Scientist 4 Divorced 2089
## 544 Manufacturing Director 3 Married 6553
## 556 Sales Executive 3 Married 9602
## 568 Sales Executive 1 Married 9714
## 570 Manufacturing Director 3 Married 13973
## 576 Manufacturing Director 3 Divorced 5993
## 579 Manufacturing Director 3 Single 5042
## 584 Sales Executive 2 Married 4779
## 603 Research Director 1 Married 11416
## 606 Research Director 3 Married 14814
## 609 Laboratory Technician 1 Single 3578
## 611 Laboratory Technician 4 Married 5363
## 612 Healthcare Representative 3 Single 6811
## 614 Sales Representative 2 Single 2121
## 616 Laboratory Technician 4 Married 3986
## 617 Laboratory Technician 2 Married 3838
## 625 Laboratory Technician 1 Single 2561
## 626 Research Scientist 3 Divorced 2517
## 630 Healthcare Representative 2 Single 8621
## 632 Sales Executive 4 Single 9888
## 644 Research Scientist 1 Married 2307
## 651 Laboratory Technician 1 Single 2450
## 655 Laboratory Technician 3 Single 3894
## 668 Laboratory Technician 4 Divorced 3408
## 669 Laboratory Technician 3 Married 4883
## 684 Healthcare Representative 4 Divorced 9439
## 686 Manufacturing Director 2 Divorced 10648
## 689 Laboratory Technician 4 Single 1102
## 695 Sales Executive 4 Divorced 9525
## 703 Sales Executive 4 Married 10377
## 704 Human Resources 2 Married 2148
## 706 Sales Executive 4 Single 6230
## 709 Laboratory Technician 3 Married 3702
## 711 Manager 1 Married 11916
## 717 Sales Executive 3 Married 6578
## 718 Research Scientist 3 Single 3660
## 721 Manufacturing Director 3 Married 9434
## 722 Manufacturing Director 3 Married 5768
## 724 Manufacturing Director 4 Single 8858
## 725 Laboratory Technician 3 Single 3968
## 735 Research Scientist 4 Single 2972
## 743 Research Scientist 4 Married 4285
## 746 Laboratory Technician 1 Divorced 2296
## 748 Healthcare Representative 1 Single 5343
## 749 Laboratory Technician 1 Divorced 3907
## 750 Research Scientist 4 Single 2500
## 751 Sales Representative 4 Single 2323
## 756 Laboratory Technician 3 Single 3708
## 759 Laboratory Technician 3 Married 3306
## 764 Sales Executive 3 Married 6347
## 780 Research Scientist 4 Married 4787
## 783 Human Resources 2 Divorced 2277
## 787 Manager 3 Married 13116
## 791 Laboratory Technician 1 Married 4789
## 798 Laboratory Technician 1 Married 2398
## 835 Healthcare Representative 2 Divorced 13577
## 837 Research Scientist 3 Married 2996
## 839 Manufacturing Director 2 Divorced 10333
## 849 Sales Executive 3 Married 6380
## 851 Sales Executive 3 Married 4559
## 855 Manufacturing Director 2 Married 4788
## 861 Laboratory Technician 3 Single 2838
## 867 Sales Executive 4 Married 4465
## MonthlyRate NumCompaniesWorked OverTime PercentSalaryHike PerformanceRating
## 5 17218 1 Yes 13 3
## 8 24223 2 Yes 14 3
## 9 18410 1 Yes 19 3
## 16 24406 1 No 13 3
## 18 16044 2 No 14 3
## 20 9364 2 No 15 3
## 26 8751 7 No 11 3
## 44 26362 8 No 22 4
## 46 16458 1 No 13 3
## 47 12147 6 No 15 3
## 50 5970 6 No 18 3
## 52 22456 3 Yes 21 4
## 56 20520 1 No 20 4
## 62 5747 3 No 12 3
## 69 5868 4 No 13 3
## 72 23490 0 No 18 3
## 73 15497 3 Yes 13 3
## 78 3156 1 No 15 3
## 80 17536 1 Yes 11 3
## 81 14382 5 No 15 3
## 89 2975 1 No 15 3
## 90 22822 3 No 12 3
## 92 11162 1 No 13 3
## 93 2396 6 Yes 15 3
## 96 24532 1 No 25 4
## 99 4668 0 No 13 3
## 109 10842 4 No 22 4
## 111 22088 1 Yes 14 3
## 115 21026 3 Yes 15 3
## 117 20623 0 No 14 3
## 130 7739 2 No 12 3
## 134 7693 4 No 21 4
## 137 6689 1 Yes 11 3
## 139 4303 7 Yes 21 4
## 140 14506 2 No 12 3
## 144 22825 4 No 14 3
## 148 10589 2 No 13 3
## 151 11512 2 No 17 3
## 158 13547 7 No 11 3
## 167 3498 3 No 16 3
## 172 2125 8 No 18 3
## 173 8984 5 No 11 3
## 175 13938 6 No 13 3
## 180 12930 4 No 22 4
## 193 26314 2 No 14 3
## 196 23231 1 No 11 3
## 197 18725 3 No 23 4
## 200 9100 1 No 21 4
## 202 7346 1 No 17 3
## 209 10415 5 No 14 3
## 213 15232 3 No 17 3
## 220 9973 1 Yes 20 4
## 233 24164 7 No 11 3
## 236 14922 5 No 11 3
## 239 19655 4 No 14 3
## 240 21981 5 No 15 3
## 244 15182 3 No 12 3
## 246 6054 6 No 14 3
## 257 3909 1 No 11 3
## 261 11882 7 No 14 3
## 266 16479 0 No 13 3
## 268 24852 1 No 12 3
## 273 7018 1 No 21 4
## 282 3119 2 Yes 13 3
## 283 9060 4 No 17 3
## 288 8758 0 No 11 3
## 301 8891 1 No 14 3
## 304 18698 1 No 19 3
## 312 20364 7 No 15 3
## 315 11924 8 No 14 3
## 316 8571 1 Yes 17 3
## 322 7660 1 No 12 3
## 323 2373 9 No 12 3
## 327 3549 0 Yes 14 3
## 336 3129 1 No 22 4
## 337 18500 1 No 11 3
## 339 17251 3 Yes 14 3
## 342 14222 1 Yes 12 3
## 355 26458 0 Yes 13 3
## 356 15891 3 No 18 3
## 359 10877 1 Yes 22 4
## 366 18256 9 No 11 3
## 370 10557 7 No 16 3
## 374 9953 1 No 11 3
## 376 24450 1 Yes 13 3
## 386 16292 1 No 13 3
## 387 9096 1 No 14 3
## 388 22645 4 No 12 3
## 396 7747 0 Yes 18 3
## 398 3072 1 No 15 3
## 402 16439 2 No 16 3
## 406 2671 4 Yes 14 3
## 410 5200 6 No 24 4
## 426 5268 5 No 13 3
## 447 9867 7 No 15 3
## 452 21158 1 No 13 3
## 458 8423 0 Yes 22 4
## 462 10950 2 No 11 3
## 466 4306 5 Yes 11 3
## 473 15067 4 Yes 14 3
## 483 26496 1 Yes 15 3
## 487 16019 1 No 13 3
## 493 23413 9 No 11 3
## 502 12947 5 Yes 14 3
## 505 26703 3 No 19 3
## 514 15146 1 Yes 12 3
## 515 2851 1 No 24 4
## 521 8306 1 No 16 3
## 527 6319 4 Yes 11 3
## 531 24608 0 No 18 3
## 533 23779 1 No 24 4
## 536 5228 4 No 14 3
## 544 7259 9 No 14 3
## 556 3010 4 Yes 11 3
## 568 5323 1 No 11 3
## 570 4161 3 Yes 18 3
## 576 2689 1 No 18 3
## 579 3140 0 No 13 3
## 584 3698 1 Yes 20 4
## 603 17802 0 Yes 12 3
## 606 13514 3 No 19 3
## 609 23577 0 No 12 3
## 611 10846 0 No 12 3
## 612 2112 2 Yes 17 3
## 614 9947 1 Yes 13 3
## 616 11912 1 No 14 3
## 617 8192 8 No 11 3
## 625 5355 7 No 11 3
## 626 3208 1 No 11 3
## 630 17654 1 No 14 3
## 632 6770 1 No 21 4
## 644 14460 1 Yes 23 4
## 651 21731 1 No 19 3
## 655 9129 5 No 16 3
## 668 6705 7 No 13 3
## 669 22845 1 No 18 3
## 684 23402 3 Yes 16 3
## 686 14394 1 No 25 4
## 689 9241 1 No 22 4
## 695 7677 1 No 14 3
## 703 13755 4 Yes 11 3
## 704 6889 0 Yes 11 3
## 706 13430 7 No 14 3
## 709 16376 1 No 11 3
## 711 25927 1 Yes 23 4
## 717 2706 1 No 18 3
## 718 7909 3 No 13 3
## 721 9606 1 No 15 3
## 722 26493 3 No 17 3
## 724 15669 0 No 11 3
## 725 13624 4 No 13 3
## 735 22061 1 No 13 3
## 743 3031 1 No 17 3
## 746 10036 0 No 14 3
## 748 25755 0 No 20 4
## 749 3622 1 No 13 3
## 750 10515 0 No 14 3
## 751 17205 1 Yes 14 3
## 756 2104 2 No 14 3
## 759 26176 7 No 19 3
## 764 24920 0 No 12 3
## 780 26124 9 Yes 14 3
## 783 22650 3 Yes 11 3
## 787 22984 2 No 11 3
## 791 23070 4 No 25 4
## 798 15999 1 Yes 17 3
## 835 25592 1 Yes 15 3
## 837 5182 7 Yes 15 3
## 839 19271 8 Yes 12 3
## 849 6110 2 Yes 12 3
## 851 24788 3 Yes 11 3
## 855 25388 0 Yes 11 3
## 861 4257 0 No 14 3
## 867 12069 0 No 18 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 5 3 80 0 6
## 8 3 80 3 8
## 9 4 80 1 1
## 16 4 80 1 9
## 18 3 80 0 7
## 20 4 80 1 10
## 26 3 80 1 26
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## 849 1 80 2 8
## 851 3 80 1 4
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## 861 2 80 0 8
## 867 1 80 0 4
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 5 2 3 6 3
## 8 5 3 1 0
## 9 2 3 1 1
## 16 2 2 9 8
## 18 2 4 1 0
## 20 5 3 2 0
## 26 5 3 22 9
## 44 2 3 1 0
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## 50 1 1 2 2
## 52 3 3 9 8
## 56 2 3 5 3
## 62 0 3 7 7
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## 72 4 3 5 4
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## 111 2 3 3 1
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## 130 5 3 19 17
## 134 3 3 5 4
## 137 1 3 1 0
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## 148 6 1 2 2
## 151 2 1 3 1
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## 342 2 3 2 2
## 355 3 4 22 14
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## 396 2 1 2 2
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## 849 6 3 6 4
## 851 2 3 2 2
## 855 2 3 3 2
## 861 6 2 7 0
## 867 2 3 3 2
## YearsSinceLastPromotion YearsWithCurrManager
## 5 1 3
## 8 0 0
## 9 0 0
## 16 0 0
## 18 0 0
## 20 2 2
## 26 3 10
## 44 0 1
## 46 5 8
## 47 0 0
## 50 2 2
## 52 1 5
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## 62 1 7
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## 117 1 1
## 130 2 8
## 134 1 0
## 137 0 0
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## 151 1 2
## 158 11 5
## 167 1 2
## 172 1 7
## 173 1 2
## 175 15 2
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## 197 0 3
## 200 0 0
## 202 0 9
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## 213 0 0
## 220 2 2
## 233 14 10
## 236 11 7
## 239 1 2
## 240 1 11
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## 257 2 2
## 261 0 0
## 266 0 3
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## 273 5 8
## 282 2 17
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## 301 5 3
## 304 0 8
## 312 5 13
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## 316 0 7
## 322 8 8
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## 327 1 10
## 336 3 8
## 337 0 2
## 339 0 0
## 342 2 2
## 355 13 5
## 356 3 12
## 359 7 8
## 366 7 7
## 370 0 7
## 374 7 7
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## 396 2 2
## 398 2 2
## 402 0 2
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## 458 1 0
## 462 1 3
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## 493 1 4
## 502 0 3
## 505 5 6
## 514 8 12
## 515 2 2
## 521 1 3
## 527 0 3
## 531 1 9
## 533 0 7
## 536 2 2
## 544 0 0
## 556 1 0
## 568 6 7
## 570 1 5
## 576 0 7
## 579 3 8
## 584 7 5
## 603 1 7
## 606 1 3
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## 612 0 7
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## 616 4 13
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## 625 0 0
## 626 0 3
## 630 7 7
## 632 2 1
## 644 3 0
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## 655 1 2
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## 711 0 8
## 717 1 4
## 718 1 7
## 721 7 8
## 722 0 2
## 724 7 8
## 725 0 0
## 735 0 0
## 743 3 7
## 746 0 0
## 748 4 9
## 749 1 2
## 750 0 2
## 751 0 2
## 756 1 4
## 759 0 0
## 764 0 13
## 780 2 2
## 783 0 3
## 787 2 2
## 791 1 2
## 798 0 0
## 835 15 0
## 837 1 3
## 839 14 10
## 849 1 0
## 851 2 2
## 855 0 2
## 861 7 7
## 867 2 2Model1_fit <- lm(MonthlyIncome ~ ., data = Attritiondata)
summary(Model1_fit)##
## Call:
## lm(formula = MonthlyIncome ~ ., data = Attritiondata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3708.8 -674.1 14.7 614.1 4100.0
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.013e+01 7.772e+02 0.077 0.938349
## ID -2.343e-01 1.476e-01 -1.588 0.112713
## Age -1.431e+00 5.649e+00 -0.253 0.800110
## AttritionYes 8.904e+01 1.154e+02 0.771 0.440729
## BusinessTravelTravel_Frequently 1.895e+02 1.420e+02 1.334 0.182441
## BusinessTravelTravel_Rarely 3.720e+02 1.200e+02 3.099 0.002005 **
## DailyRate 1.452e-01 9.129e-02 1.591 0.112062
## DepartmentResearch & Development 1.234e+02 4.768e+02 0.259 0.795866
## DepartmentSales -4.594e+02 4.877e+02 -0.942 0.346580
## DistanceFromHome -6.237e+00 4.578e+00 -1.362 0.173417
## Education -3.743e+01 3.716e+01 -1.007 0.314105
## EducationFieldLife Sciences 1.352e+02 3.692e+02 0.366 0.714248
## EducationFieldMarketing 1.377e+02 3.914e+02 0.352 0.725050
## EducationFieldMedical 3.326e+01 3.699e+02 0.090 0.928376
## EducationFieldOther 9.152e+01 3.946e+02 0.232 0.816664
## EducationFieldTechnical Degree 9.680e+01 3.843e+02 0.252 0.801179
## EmployeeCount NA NA NA NA
## EmployeeNumber 8.681e-02 6.103e-02 1.422 0.155269
## EnvironmentSatisfaction -6.267e+00 3.364e+01 -0.186 0.852252
## GenderMale 1.100e+02 7.442e+01 1.478 0.139715
## HourlyRate -3.591e-01 1.824e+00 -0.197 0.844003
## JobInvolvement 1.677e+01 5.321e+01 0.315 0.752698
## JobLevel 2.783e+03 8.340e+01 33.375 < 2e-16 ***
## JobRoleHuman Resources -2.053e+02 5.157e+02 -0.398 0.690663
## JobRoleLaboratory Technician -5.891e+02 1.714e+02 -3.437 0.000618 ***
## JobRoleManager 4.280e+03 2.830e+02 15.122 < 2e-16 ***
## JobRoleManufacturing Director 1.809e+02 1.696e+02 1.067 0.286497
## JobRoleResearch Director 4.077e+03 2.193e+02 18.592 < 2e-16 ***
## JobRoleResearch Scientist -3.494e+02 1.705e+02 -2.049 0.040790 *
## JobRoleSales Executive 5.263e+02 3.576e+02 1.472 0.141449
## JobRoleSales Representative 8.531e+01 3.918e+02 0.218 0.827703
## JobSatisfaction 3.278e+01 3.344e+01 0.980 0.327278
## MaritalStatusMarried 6.708e+01 1.002e+02 0.669 0.503497
## MaritalStatusSingle 1.128e+01 1.361e+02 0.083 0.933978
## MonthlyRate -9.505e-03 5.143e-03 -1.848 0.064946 .
## NumCompaniesWorked 5.421e+00 1.691e+01 0.321 0.748622
## OverTimeYes -1.394e+01 8.434e+01 -0.165 0.868787
## PercentSalaryHike 2.586e+01 1.581e+01 1.635 0.102351
## PerformanceRating -3.235e+02 1.614e+02 -2.004 0.045368 *
## RelationshipSatisfaction 1.640e+01 3.339e+01 0.491 0.623375
## StandardHours NA NA NA NA
## StockOptionLevel -2.758e+00 5.740e+01 -0.048 0.961695
## TotalWorkingYears 5.080e+01 1.098e+01 4.627 4.3e-06 ***
## TrainingTimesLastYear 2.436e+01 2.912e+01 0.837 0.403111
## WorkLifeBalance -3.472e+01 5.161e+01 -0.673 0.501284
## YearsAtCompany -2.750e+00 1.370e+01 -0.201 0.840925
## YearsInCurrentRole 3.398e+00 1.711e+01 0.199 0.842584
## YearsSinceLastPromotion 3.084e+01 1.532e+01 2.013 0.044405 *
## YearsWithCurrManager -2.691e+01 1.669e+01 -1.613 0.107210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1055 on 823 degrees of freedom
## Multiple R-squared: 0.9501, Adjusted R-squared: 0.9473
## F-statistic: 340.7 on 46 and 823 DF, p-value: < 2.2e-16#Model1_Preds = predict(Model1_fit, newdata = NoSalary)
#as.data.frame(Model1_Preds)
#write.csv(Model1_Preds,"Model1PredictionsNoSalaryRenuKarthikeyan")
#Cross Validation and Mean Square Predictor Error Calculation
numMSPEs = 1000
MSPEHolderModel1 = numeric(numMSPEs)
MSPEHolderModel2 = numeric(numMSPEs)
RMSEHolderModel1 = numeric(numMSPEs)
RMSEHolderModel2 = numeric(numMSPEs)
for (i in 1:numMSPEs)
{
TrainObs = sample(seq(1,dim(Attritiondata)[1]),round(.8*dim(Attritiondata)[1]),replace = FALSE)
SalaryTrain = Attritiondata[TrainObs,]
SalaryTrain
SalaryTest = Attritiondata[-TrainObs,]
SalaryTest
Model1_fit <- lm(MonthlyIncome ~ ., data = SalaryTrain)
Model1_Preds = predict(Model1_fit, newdata = SalaryTest)
#MSPE Model 1
MSPE = mean((SalaryTest$MonthlyIncome - Model1_Preds)^2)
MSPE
MSPEHolderModel1[i] = MSPE
RMSEHolderModel1[i] = sqrt(MSPE)
#Model 2
Model2_fit = lm(MonthlyIncome ~ NumCompaniesWorked + Age + Gender + MaritalStatus + JobInvolvement + JobRole + DistanceFromHome + JobLevel + Education, data = SalaryTrain)
Model2_Preds = predict(Model2_fit,newdata = SalaryTest)
MSPE = mean((SalaryTest$MonthlyIncome - Model2_Preds)^2)
MSPE
MSPEHolderModel2[i] = MSPE
RMSEHolderModel2[i] = sqrt(MSPE)
}
mean(MSPEHolderModel1)## [1] 1196649 mean(MSPEHolderModel2)## [1] 1194877 mean(RMSEHolderModel1)## [1] 1091.936 mean(RMSEHolderModel2)## [1] 1091.156- Data was split 80/20 – for training and testing
- Ran both model 1 and 2 to predict Monthly Income on the testing set
- Cross Validation Process with 1000 iterations where MSPE and RMSE are calculated for both models
- Summary Statistics of mean MSPE and mean RMSE for both models
Conclusion: Model 2 is the better fit as it has a lower mean RMSE and lower mean MSPE
KNN and Naive Bayes
When I initially attempted to use all predictors for KNN, each time, the model would unfortunately run into errors and say “Warning: NAs introduced by coercionError in knn(train_predictors, test_predictors, response_train, prob = TRUE,: NA/NaN/Inf in foreign function call (arg 6)”, although there are no missing values in the Attritiondata data set. I later realized that it was due to NAs being assigned to the categorical variables during the KNN chunk. I created dummy columns for these categorical variables to get the KNN to function without running into errors. Below is the code used to create the dummy columns from the FastDummies package in R.
Preparation of the Data - Including Dummy Columns for Categorical Variables
Attritiondata$BusinessTravel<- as.factor(Attritiondata$BusinessTravel)
Attritiondata$Department<- as.factor(Attritiondata$Department)
Attritiondata$EducationField<- as.factor(Attritiondata$EducationField)
Attritiondata$Gender<- as.factor(Attritiondata$Gender)
Attritiondata$JobRole<- as.factor(Attritiondata$JobRole)
Attritiondata$MaritalStatus<- as.factor(Attritiondata$MaritalStatus)
Attritiondata$OverTime<- as.factor(Attritiondata$OverTime)
Attritiondata<-dummy_cols(Attritiondata,select_columns=c("BusinessTravel","MaritalStatus","JobRole","Department","EducationField","OverTime","Gender"))
Attritiondata <- Attritiondata %>% select(-c("BusinessTravel","MaritalStatus","JobRole","Department","EducationField","OverTime","Gender"))
#Doing Same for Attrition Test Data Set (AttritionTest)
AttritionTest$BusinessTravel<- as.factor(AttritionTest$BusinessTravel)
AttritionTest$Department<- as.factor(AttritionTest$Department)
AttritionTest$EducationField<- as.factor(AttritionTest$EducationField)
AttritionTest$Gender<- as.factor(AttritionTest$Gender)
AttritionTest$JobRole<- as.factor(AttritionTest$JobRole)
AttritionTest$MaritalStatus<- as.factor(AttritionTest$MaritalStatus)
AttritionTest$OverTime<- as.factor(AttritionTest$OverTime)
AttritionTest<-dummy_cols(AttritionTest,select_columns=c("BusinessTravel","MaritalStatus","JobRole","Department","EducationField","OverTime","Gender"))
AttritionTest <- AttritionTest %>% select(-c("BusinessTravel","MaritalStatus","JobRole","Department","EducationField","OverTime","Gender"))
diff_columns_df1 <- setdiff(names(Attritiondata), names(AttritionTest))
cat("Columns in df2 but not in df1:", paste(diff_columns_df1, collapse = ", "), "\n")## Columns in df2 but not in df1: AttritionKNN Model and Confusion Matrix - Training with All predictors
set.seed(1234)
iterations <- 100
numks <- 10
splitPerc <- 0.8
masterAcc <- matrix(nrow = iterations, ncol = numks)
for (j in 1:iterations) {
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
response_variable <- "Attrition"
response_train <- factor(train[[response_variable]])
response_test <- factor(test[[response_variable]])
# Select columns for predictors
selected_columns <- c(1, 2, 4:36) # Adjust this range as needed
#selected_columns <- c("ID","Age","BusinessTravel","DailyRate","Department", "DistanceFromHome", "Education", "EducationField", "EmployeeCount", "EmployeeNumber","EnvironmentSatisfaction", "Gender", "HourlyRate", "JobInvolvement", "JobLevel", "JobRole", "JobSatisfaction", "MaritalStatus", "MonthlyIncome", "MonthlyRate", "NumCompaniesWorked", "OverTime", "PercentSalaryHike", "PerformanceRating", "RelationshipSatisfaction", "StandardHours", "StockOptionLevel", "TotalWorkingYears", "TrainingTimesLastYear", "WorkLifeBalance", "YearsAtCompany", "YearsInCurrentRole", "YearsSinceLastPromotion", "YearsWithCurrManager")
# Extract the selected columns
train_predictors <- train[, selected_columns, drop = FALSE]
test_predictors <- test[, selected_columns, drop = FALSE]
train_predictors <- apply(train_predictors, 2, as.numeric)
test_predictors <- apply(test_predictors, 2, as.numeric)
train_predictors <- scale(train_predictors)
test_predictors <- scale(test_predictors)
# Convert to numeric matrices
train_predictors <- as.matrix(train_predictors)
test_predictors <- as.matrix(test_predictors)
# Remove infinite values
train_predictors[!is.finite(train_predictors)] <- 0
test_predictors[!is.finite(test_predictors)] <- 0
if (sum(response_train == "Yes") > 0 && sum(response_test == "Yes") > 0) {
for (i in 1:numks) {
classifications <- knn(train_predictors, test_predictors, response_train, prob = TRUE, k = i)
table(classifications, response_test)
CM_AllK<- confusionMatrix(table(classifications, response_test), positive = "Yes")
masterAcc[j, i] <- CM_AllK$overall[1]
}
}
}
CM_AllK## Confusion Matrix and Statistics
##
## response_test
## classifications No Yes
## No 143 26
## Yes 2 3
##
## Accuracy : 0.8391
## 95% CI : (0.7759, 0.8903)
## No Information Rate : 0.8333
## P-Value [Acc > NIR] : 0.4685
##
## Kappa : 0.134
##
## Mcnemar's Test P-Value : 1.383e-05
##
## Sensitivity : 0.10345
## Specificity : 0.98621
## Pos Pred Value : 0.60000
## Neg Pred Value : 0.84615
## Prevalence : 0.16667
## Detection Rate : 0.01724
## Detection Prevalence : 0.02874
## Balanced Accuracy : 0.54483
##
## 'Positive' Class : Yes
## MeanAcc = colMeans(masterAcc); MeanAcc## [1] 0.7946552 0.7893103 0.8301724 0.8294828 0.8386782 0.8394828 0.8433333
## [8] 0.8406897 0.8425287 0.8426437plot(seq(1, numks, 1), MeanAcc, type = "l", ylab = "Mean Accuracy (Positive Class: Yes)")
which.max(MeanAcc)## [1] 7max(MeanAcc)## [1] 0.8433333From the plot, we see that the best k is k = 7. The overall accuracy is 83.91%, but sensitivity (True Positive Rate) is low (10.35%).The model is better at correctly predicting the majority class (“No”) but struggles with the minority class (“Yes”).
Looking specifically at the Confusion Matrix statistics, this is the output and the interpretation of each of these statistics: - Sensitivity (True Positive Rate): 0.10345 The proportion of actual positives correctly predicted for those who attrited. - Specificity (True Negative Rate): 0.98621 The proportion of actual negatives correctly predicted (for those who did not leave the company) - Positive Predictive Value (Precision): 0.60000 The proportion of predicted positives that are true positives (attrited correctly identified as attrited) - Negative Predictive Value: 0.84615 The proportion of predicted negatives that are true negatives. (not attrited correctly identified as not attrited) - Prevalence: 0.16667 The proportion of actual positives in the dataset. (proportion of attrited in the overall dataset)
TRYING THRESHOLD CHANGE for KNN - All predictors; k = 7
set.seed(1234)
iterations<- 100
accuracy_table <- numeric(iterations)
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
accuracy_table <- numeric(iterations)
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
train_features<- train[, -which(names(train) == "Attrition")]
test_features<- test[, -which(names(test) == "Attrition")]
train_target<- train$Attrition
# train_scaled<- scale(train_features) Using this in the knn returns "No missing values allowed"
#test_scaled<- scale(test_features)
for (i in 1:iterations) {
classifications <- knn(train_features, test_features, train_target, prob = TRUE, k = 7)
table(classifications, response_test)
CM_AllK7 <- confusionMatrix(table(as.factor(test$Attrition),classifications), positive ="Yes")
accuracy_table[i] <- CM_AllK7$overall[1]
}
#print(accuracy_table)
avg_accuracy<-mean(accuracy_table[1])
avg_accuracy## [1] 0.7988506specificity_table<- numeric(iterations)
sensitivity_table<- numeric(iterations)
for (i in 1:iterations) {
classifications <- knn(train_features, test_features, train_target, prob = TRUE, k = 7)
table(classifications, response_test)
CM_AllK7 <- confusionMatrix(table(as.factor(test$Attrition),classifications), positive ="Yes")
specificity_table[i] <- CM_AllK7$byClass['Specificity']
sensitivity_table[i] <- CM_AllK7$byClass['Sensitivity']
}
CM_AllK7## Confusion Matrix and Statistics
##
## classifications
## No Yes
## No 137 3
## Yes 32 2
##
## Accuracy : 0.7989
## 95% CI : (0.7315, 0.8557)
## No Information Rate : 0.9713
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0552
##
## Mcnemar's Test P-Value : 2.214e-06
##
## Sensitivity : 0.40000
## Specificity : 0.81065
## Pos Pred Value : 0.05882
## Neg Pred Value : 0.97857
## Prevalence : 0.02874
## Detection Rate : 0.01149
## Detection Prevalence : 0.19540
## Balanced Accuracy : 0.60533
##
## 'Positive' Class : Yes
## avg_specificity<-mean(specificity_table[1])
avg_specificity## [1] 0.8106509avg_sensitivity<-mean(sensitivity_table[1])
avg_sensitivity## [1] 0.4###### New Threshold using classifications which used k = 7 from above
#classifications
#attributes(classifications) # Look at possible attributes
#attributes(classifications)$prob # Probability of what was classified for that observation
probs = ifelse(classifications == "Yes",attributes(classifications)$prob, 1- attributes(classifications)$prob)
summary(Attritiondata$Attrition)## No Yes
## 730 140140/(730+140) #16.09%## [1] 0.1609195NewClass = ifelse(probs > .1609, "Yes", "No")
NewClass <- factor(NewClass, levels = levels(response_test))
table(NewClass,response_test)## response_test
## NewClass No Yes
## No 115 18
## Yes 30 11CM_NewThreshold <- confusionMatrix(table(NewClass, response_test), positive = "Yes", mode = "everything")
CM_NewThreshold## Confusion Matrix and Statistics
##
## response_test
## NewClass No Yes
## No 115 18
## Yes 30 11
##
## Accuracy : 0.7241
## 95% CI : (0.6514, 0.7891)
## No Information Rate : 0.8333
## P-Value [Acc > NIR] : 0.9999
##
## Kappa : 0.1479
##
## Mcnemar's Test P-Value : 0.1124
##
## Sensitivity : 0.37931
## Specificity : 0.79310
## Pos Pred Value : 0.26829
## Neg Pred Value : 0.86466
## Precision : 0.26829
## Recall : 0.37931
## F1 : 0.31429
## Prevalence : 0.16667
## Detection Rate : 0.06322
## Detection Prevalence : 0.23563
## Balanced Accuracy : 0.58621
##
## 'Positive' Class : Yes
## Similar to before, the loop ran for 100 iterations, but k was set to 7 when it came to knn. There are 2 confusion matrices. We have one, without the threshold changes, and one with the threshold change. The accuracy went down with the threshold change, while sensitivity reduced by a lot, and specificity reduced by ~10%. The positive predictive value increased by 9%, and the negative pred value reduced by 14%.
The new threshold has a lower accuracy compared to the original kNN classification.Sensitivity is significantly lower for the new threshold, indicating that fewer true positives are captured.Specificity is slightly lower for the new threshold, indicating a decrease in correctly identified true negatives. Precision is lower for the new threshold, reflecting a decrease in the accuracy of positive predictions. The original KNN classification has a higher accuracy and sensitivity compared to the new threshold.
#Applying to the Test Model to predict attrition using KNN with all predictors (This is best model)
train_features<- Attritiondata[, -which(names(Attritiondata) == "Attrition")]
test_features<- AttritionTest[]#[, -which(names(AttritionTest)=="Attrition")]
#head(test_features,5)
train_target <- Attritiondata$Attrition
AttritionClassifications <- knn(train_features, test_features, train_target, prob = TRUE, k = 7)
AttritionTest$Attrition <- AttritionClassifications
#head(AttritionTest,5)
AttritionPredictionsKNN<-AttritionTest%>%select(c("ID","Attrition"))
#head(AttritionPredictionsKNN,5)
write.csv(AttritionPredictionsKNN,"CaseStudy2AttritionPredictionsKNN_RenuKarthikeyan.csv", row.names = T)KNN Using Select Predictors
After doing my Exploratory Data Analysis, I believe the important predictors for Attrition are: Gender, Department,Job Satisfaction,Distance From Home,Monthly Income, Job Level, Age, Over Time, Percent Salary Hike, Performance Rating, and Education.
set.seed(1234)
iterations = 100
numks = 10
splitPerc = .8
masterAcc = matrix(nrow = iterations, ncol = numks)
for (j in 1:iterations) {
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
response_train <- factor(train$Attrition)
response_test <- factor(test$Attrition)
#UPDATE HERE!!!!
#train_predictors <- train[, c(2,6,7,8,13,16,18,20,24,25,26)]
#test_predictors <- test[, c(2,6,7,8,13,16,18,20,24,25,26) ]
selected_cols <- c(2, 6, 7, 8, 13, 16, 18, 20, 24, 25, 26)
train_predictors <- train[, selected_cols] #subset function
test_predictors <- test[, selected_cols]
train_predictors <- apply(train_predictors, 2, as.numeric)
test_predictors <- apply(test_predictors, 2, as.numeric)
train_predictors <- scale(train_predictors)
test_predictors <- scale(test_predictors)
train_predictors <- as.matrix(train_predictors)
test_predictors <- as.matrix(test_predictors)
# Remove infinite values
train_predictors[!is.finite(train_predictors)] <- 0
test_predictors[!is.finite(test_predictors)] <- 0
for (i in 1:numks) {
classifications <- knn(train_predictors, test_predictors, response_train, prob =TRUE, k = i)
table(classifications, response_test)
CM_Select<- confusionMatrix(table(classifications, response_test), positive = "Yes")
masterAcc[j, i] <- CM_Select$overall[1]
}
}
CM_Select## Confusion Matrix and Statistics
##
## response_test
## classifications No Yes
## No 144 24
## Yes 4 2
##
## Accuracy : 0.8391
## 95% CI : (0.7759, 0.8903)
## No Information Rate : 0.8506
## P-Value [Acc > NIR] : 0.7085939
##
## Kappa : 0.0731
##
## Mcnemar's Test P-Value : 0.0003298
##
## Sensitivity : 0.07692
## Specificity : 0.97297
## Pos Pred Value : 0.33333
## Neg Pred Value : 0.85714
## Prevalence : 0.14943
## Detection Rate : 0.01149
## Detection Prevalence : 0.03448
## Balanced Accuracy : 0.52495
##
## 'Positive' Class : Yes
## MeanAcc = colMeans(masterAcc); MeanAcc## [1] 0.7545977 0.7523563 0.8077586 0.8095977 0.8272414 0.8263793 0.8315517
## [8] 0.8305172 0.8338506 0.8359195plot(seq(1, numks, 1), MeanAcc, type = "l", ylab = "Mean Accuracy (Positive Class: Yes)")
which.max(MeanAcc)## [1] 10max(MeanAcc)## [1] 0.8359195We see that the best k is k = 10. The confusion matrix is taking an average of all the k’s tried. Here, the accuracy is 83.91%, similar to the initial average confusion matrix seen for all predictors using KNN. The sensitivity is quite low at 7.69%, suggesting that the model is struggling to correctly identify positive (true Attrition) instances. The model shows high specificity (97.30%), indicating a decent ability to correctly identify negative instances (not attrition). The positive predictive value (precision) is at 33.33%, indicating that among instances predicted as positive, about one-third are true positives.
TRYING k = 10 for KNN - Select predictors
iterations<- 100
accuracy_table <- numeric(iterations)
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
train_features<- train[, selected_cols]
test_features<- test[, selected_cols]
train_target<- train$Attrition
train_scaled<- scale(train_features)
test_scaled<- scale(test_features)
for (i in 1:iterations) {
classifications <- knn(train_features, test_features, train_target, prob = TRUE, k = 10)
table(classifications, response_test)
CM_SelectK <- confusionMatrix(table(as.factor(test$Attrition),classifications), positive ="Yes")
accuracy_table[i] <- CM_SelectK$overall[1]
}
#print(accuracy_table)
avg_accuracy<-mean(accuracy_table[1]); avg_accuracy## [1] 0.8275862specificity_table<- numeric(iterations)
sensitivity_table<- numeric(iterations)
for (i in 1:iterations) {
classifications <- knn(train_features, test_features, train_target, prob = TRUE, k = 10)
table(classifications, response_test)
CM_SelectK<- confusionMatrix(table(as.factor(test$Attrition),classifications), positive ="Yes")
specificity_table[i] <- CM_SelectK$byClass['Specificity']
sensitivity_table[i] <- CM_SelectK$byClass['Sensitivity']
}
CM_SelectK## Confusion Matrix and Statistics
##
## classifications
## No Yes
## No 145 0
## Yes 29 0
##
## Accuracy : 0.8333
## 95% CI : (0.7695, 0.8854)
## No Information Rate : 1
## P-Value [Acc > NIR] : 1
##
## Kappa : 0
##
## Mcnemar's Test P-Value : 1.999e-07
##
## Sensitivity : NA
## Specificity : 0.8333
## Pos Pred Value : NA
## Neg Pred Value : NA
## Prevalence : 0.0000
## Detection Rate : 0.0000
## Detection Prevalence : 0.1667
## Balanced Accuracy : NA
##
## 'Positive' Class : Yes
## avg_specificity<-mean(specificity_table[1])
avg_specificity## [1] 0.8323699avg_sensitivity<-mean(sensitivity_table[1])
avg_sensitivity## [1] 0We see the average specificity is 83.43% and sensitivity is 0%. This model’s performance is notably poor, with zero sensitivity, meaning it failed to correctly identify any positive instances. The specificity and negative predictive value are relatively high, but the lack of sensitivity indicates a serious limitation in identifying instances of the positive class. This suggests that the model might need further refinement or a different approach to address the imbalance and improve its ability to correctly classify positive instances.
Naive Bayes and Confusion Matrix - Training with All predictors
set.seed(1234)
iterations = 100
masterAcc = matrix(nrow = iterations)
splitPerc = .8 #Training / Test split Percentage
for(j in 1:iterations)
{
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
train$Attrition <- factor(train$Attrition, levels = c("Yes", "No"))
test$Attrition <- factor(test$Attrition, levels = c("Yes", "No"))
model <- naiveBayes(train[, -3], as.factor(train$Attrition), laplace = 1)
predictions <- predict(model, test[, -3])
confMatrix <- table(predictions, as.factor(test$Attrition))
CM_NB_All <- confusionMatrix(confMatrix)
masterAcc[j] <- CM_NB_All$overall[1]
}
CM_NB_All## Confusion Matrix and Statistics
##
##
## predictions Yes No
## Yes 23 92
## No 2 57
##
## Accuracy : 0.4598
## 95% CI : (0.3841, 0.5368)
## No Information Rate : 0.8563
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.1211
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.9200
## Specificity : 0.3826
## Pos Pred Value : 0.2000
## Neg Pred Value : 0.9661
## Prevalence : 0.1437
## Detection Rate : 0.1322
## Detection Prevalence : 0.6609
## Balanced Accuracy : 0.6513
##
## 'Positive' Class : Yes
## MeanAcc = colMeans(masterAcc); MeanAcc## [1] 0.6482184Given all the predictors, the sensitivity rate of the model is the percentage of actual attrition cases correctly identified. It measures the model’s ability to capture employees who are truly at risk of attrition among all employees who actually attrite. This model shows imbalanced performance with high sensitivity (high attrition) but low specificity. It performs well in identifying actual positive instances (attrition) but struggles to correctly identify negative instances(no attrition). The positive predictive value is relatively low, indicating that when it predicts a positive instance, it has a 20% chance of being correct.
Naive Bayes and Confusion Matrix - Training; Using Select predictors to determine Attrition
set.seed(1234)
iterations = 100
masterAcc = matrix(nrow = iterations)
splitPerc = .8 #Training / Test split Percentage
for(j in 1:iterations)
{
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
train$Attrition <- factor(train$Attrition, levels = c("Yes", "No"))
test$Attrition <- factor(test$Attrition, levels = c("Yes", "No"))
model2 <- naiveBayes(train[, c(2, 6, 7, 8, 13, 16, 18, 20, 24, 25, 26)], as.factor(train$Attrition), laplace = 1)
predictions <- predict(model2, test[, c(2, 6, 7, 8, 13, 16, 18, 20, 24, 25, 26)])
confMatrix <- table(predictions, as.factor(test$Attrition))
CM_NB_Select <- confusionMatrix(confMatrix)
masterAcc[j] <- CM_NB_Select$overall[1]
}
CM_NB_Select## Confusion Matrix and Statistics
##
##
## predictions Yes No
## Yes 3 4
## No 22 145
##
## Accuracy : 0.8506
## 95% CI : (0.7888, 0.9)
## No Information Rate : 0.8563
## P-Value [Acc > NIR] : 0.6357048
##
## Kappa : 0.133
##
## Mcnemar's Test P-Value : 0.0008561
##
## Sensitivity : 0.12000
## Specificity : 0.97315
## Pos Pred Value : 0.42857
## Neg Pred Value : 0.86826
## Prevalence : 0.14368
## Detection Rate : 0.01724
## Detection Prevalence : 0.04023
## Balanced Accuracy : 0.54658
##
## 'Positive' Class : Yes
## MeanAcc = colMeans(masterAcc)
MeanAcc## [1] 0.8312069#use predict function on the "validation" sets. Use the same model. Test will be validation set.
Predictions<- predict(model2,AttritionTest[,c(2, 5, 6, 7, 12, 15, 17, 19, 23, 24, 25)])
AttritionTest$Attrition<- Predictions
#View(AttritionTest$Attrition)
AttritionPredictionsNB<- AttritionTest %>% select(c("ID","Attrition"))
write.csv(AttritionPredictionsNB,"CaseStudy2AttritionPredictionsNB_RenuKarthikeyan.csv", row.names = T)Sensitivity is 12%; 12% of actual attrition cases are correctly identified by the model. This suggests that the model may not be very effective at capturing employees who are truly at risk of attrition. This model has a higher accuracy of 85.06%, and has imbalanced performance with low sensitivity and high specificity. However, accuracy can be misleading, especially in imbalanced datasets where one class (e.g., “No attrition”) dominates. In this case, accuracy is not the best metric to evaluate the model’s performance. Positive predictive value (PPV) is at 42.86%. This indicates that when the model predicts attrition, there’s a 42.86% chance that the prediction is correct. It reflects the precision of the model in identifying true positive cases among all instances predicted as positive. The low prevalence (the proportion of actual positive cases in the dataset) of attrition, is 14.37%. This low prevalence contributes to the imbalanced nature of the performance metrics.
Best Model Determination from the Models tried for Attrition (KNN and Naive Bayes)
It looks like the KNN model at k = 7 with all predictors without threshold adjustment has better accuracy within the KNN models. The Naive Bayes model with select predictors has the highest accuracy, and is better than the other Naive Bayes model which included all predictors.
Naive Bayes has a higher accuracy (85.06%) compared to KNN (79.89%). However, accuracy alone may not be the most informative metric, especially in imbalanced datasets.KNN has a higher sensitivity (40.00%) compared to Naive Bayes (12.00%). Sensitivity is crucial when identifying cases of attrition, as it represents the proportion of true positive cases among all actual positive cases.Naive Bayes has higher specificity (97.32%) compared to KNN (81.07%). Specificity is important when minimizing false positives, but it’s essential to balance it with sensitivity.Naïve Bayes has a higher positive predictive value (precision) at 42.86%, while KNN has a lower precision at 5.88%. Precision indicates the accuracy of positive predictions.
Of the 2 best models (best in KNN and best in Naive Bayes), I think the Naive Bayes is the better model to predict Attrition, given the high positive prediction value(precision), accuracy, and narrower confidence interval.
Thank You
This concludes this presentation and analysis. Thank you for your time and I look forward to empowering Frito Lay with data-driven wisdom. I created a shiny app to visualize and notice insights regarding the attrition data. Please feel free to look into the link provided. If you have any questions, please feel free to reach out to me, my email is in the attached PowerPoint presentation. Thank you!